Publications

Political polarization is often understood in terms of extreme issue positions. But polarization can only emerge if issue positions are aligned into a single ideological spectrum, ranging from left/ liberal to right/conservative. It is unclear how a high-dimensional space of policy issues can organize itself into a single ideological spectrum and give rise to polarization. We explain this phenomenon using Weighted Balance Theory (WBT), which describes the interaction of issue positions and interpersonal affect. By implementing WBT into an agent-based opinion dynamics model, we generate a single ideological spectrum from an arbitrarily high dimensional issue space. Furthermore, we show that WBT outperforms other models in predicting respondents’ attitudes in 44 years worth of empirical data from the ANES survey. A calibrated version of our model can reproduce properties of empirically observed opinion distributions.

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

A landmark collaboration shows that Facebook’s news feed filters partisan political news to users with the same views. But changing the feed algorithm to reduce exposure to like-minded content does not reduce political polarization.

Disasters are extraordinary shocks that disrupt every aspect of the community life. Lives are lost, infrastructure is destroyed, the social fabric is torn apart, and people are left with physical and psychological trauma. In the aftermath of a disaster, communities begin the collective process of healing, grieving losses, repairing damage, and adapting to a new reality. Previous work has suggested the existence of a series of prototypical stages through which such community responses evolve. As social media have become more widely used, affected communities have increasingly adopted them to express, navigate, and build their response due to the greater visibility and speed of interaction that these platforms afford. In this study, we ask if the behavior of disaster-struck communities on social media follows prototypical patterns and what relationship, if any, these patterns may have with those established for offline behavior in previous work. Building on theoretical models of disaster response, we investigate whether, in the short term, community responses on social media in the aftermath of disasters follow a prototypical trajectory. We conduct our analysis using computational methods to model over 200 disaster-stricken U.S. communities. Community responses are measured in a range of domains, including psychological, social, and sense-making, and as multidimensional time series derived from the linguistic markers in tweets from those communities. We find that community responses on Twitter demonstrate similar response patterns across numerous social, aspirational, and physical dynamics. Additionally, through cluster analysis, we demonstrate that a minority of communities are characterized by more intense and enduring emotional coping strategies and sense-making. In this investigation of the relationship between community response and intrinsic properties of disasters, we reveal that the severity of the impact makes the deviant trajectory more likely, while the type and duration of a disaster are not associated with it.

The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA’s robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer.

The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who ‘speak their mind’ are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence. By analysing communications by members of the US Congress on Twitter between 2011 and 2022, we show that politicians’ conception of honesty has undergone a distinct shift, with authentic belief speaking that may be decoupled from evidence becoming more prominent and more differentiated from explicitly evidence-based fact speaking. We show that for Republicans—but not Democrats—an increase in belief speaking of 10% is associated with a decrease of 12.8 points of quality (NewsGuard scoring system) in the sources shared in a tweet. In contrast, an increase in fact-speaking language is associated with an increase in quality of sources for both parties. Our study is observational and cannot support causal inferences. However, our results are consistent with the hypothesis that the current dissemination of misinformation in political discourse is linked to an alternative understanding of truth and honesty that emphasizes invocation of subjective belief at the expense of reliance on evidence.

This article presents a progress report from the last two years of the Teaching Digital Thinking (TDT) project. This project aims to implement new concepts, didactic methods, and teaching formats for sustainable digital transformation in Austrian Universities’ curricula by introducing new digital competencies. By equipping students and teachers with 21st-century digital competencies, partner universities can contribute to solving global challenges and organizing pilot projects. In line with the overall project aims, this article presents the ongoing digital transformation activities, courses, and research in the project, which have been carried out by the five partner universities since 2020, and briefly discusses the results. This article presents a summary of the research and educational activities carried out within two parts: complementary research and pilot projects.

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dc.contributor.author: Niederkrotenthaler, Thomas; Tran, Ulrich S; Baginski, Hubert; Sinyor, Mark; Strauss, Markus J; Sumner, Steven A; Voracek, Martin; Till, Benedikt; Murphy, Sean

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dc.contributor.author: Galesic, Mirta; Olsson, Henrik

Increased sharing of untrustworthy information on social media platforms is one of the main challenges of our modern information society. Because information disseminated by political elites is known to shape citizen and media discourse, it is particularly important to examine the quality of information shared by politicians. Here, we show that from 2016 onward, members of the Republican Party in the US Congress have been increasingly sharing links to untrustworthy sources. The proportion of untrustworthy information posted by Republicans versus Democrats is diverging at an accelerating rate, and this divergence has worsened since President Biden was elected. This divergence between parties seems to be unique to the United States as it cannot be observed in other western democracies such as Germany and the United Kingdom, where left–right disparities are smaller and have remained largely constant.

Background: There is accumulating evidence about detrimental impacts of the pandemic on population mental health, but knowledge on risk of groups specifically affected by the pandemic and variations across time is still limited.
Methods: We surveyed approximately n=1,000 Austrian residents in 12 waves between April and December 2020 (n=12,029). Outcomes were suicidal ideation (Beck Suicidal Ideation Scale), depressive symptoms (Patient Health Questionnaire-9), anxiety (Hospital Anxiety Depression Scale), and domestic violence. We also assessed the perceived burden from the pandemic. Demographic and Covid-19 specific occupational and morbidity-related variables were used to explain outcomes in multivariable regression analyses, controlling for well-established risk factors of mental ill-health, and variations over time were analyzed.
Results: Young age, working in healthcare or from home, and own Covid-19 illness were consistent risk factors controlling for a wide range of known mental health risk factors. Time patterns in the perceived burden from Covid-19-related measures were consistent with the time sequence of restrictions and relaxations of governmental measures. Depressive and anxiety symptoms were relatively stable over time, with some increase of depression during the second phase of lockdowns. Domestic violence increased immediately after both hard lockdowns. Suicidal ideation decreased slightly over time, with a low during the second hard lockdown. Mental health indicators for women and young people showed some deterioration over time, whereas those reporting own Covid-19 illness improved.
Limitations: Data from before the pandemic were not available.
Conclusions: Among mental health outcomes, increases in domestic violence and, to some smaller extent, depressive symptoms, appeared most closely related to the timing of hard lockdowns. Healthcare staff, individuals working from home, those with Covid-19, as well as young people and women are non-traditional risk groups who warrant heightened attention in prevention during and in the aftermath of the pandemic.

We examine the patterns of medical preprint sharing on Twitter during the early stages of the COVID-19 pandemic. Our analysis demonstrates a stark increase in attention to medical preprints among the general public since the beginning of the pandemic. We also observe a political divide in medical preprint sharing patterns - a finding in line with previous observations regarding the politicisation of COVID-19-related discussions. In addition, we find that the increase in attention to preprints from the members of the general public coincided with the change in the social media-based discourse around preprints.

Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.

Background: Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are lacking. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of the content posted on these platforms.
Objective: This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods: We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multiclass and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (term frequency-inverse document frequency with a linear support vector machine) and 2 state-of-the-art deep learning models (Bidirectional Encoder Representations from Transformers [BERT] and XLNet). The first task classified posts into 6 main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either suicidal ideation and attempts or coping and recovery, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these 5 categories. The second classification task was binary and separated posts in the 11 categories referring to actual suicide from posts in the off-topic category, which use suicide-related terms in another meaning or context.
Results: In both tasks, the performance of the 2 deep learning models was very similar and better than that of the majority or the word frequency classifier. BERT and XLNet reached accuracy scores above 73% on average across the 6 main categories in the test set and F1-scores between 0.69 and 0.85 for all but the suicidal ideation and attempts category (F1=0.55). In the binary classification task, they correctly labeled around 88% of the tweets as about suicide versus off-topic, with BERT achieving F1-scores of 0.93 and 0.74, respectively. These classification performances were similar to human performance in most cases and were comparable with state-of-the-art models on similar tasks.
Conclusions: The achieved performance scores highlight machine learning as a useful tool for media effects research on suicide. The clear advantage of BERT and XLNet suggests that there is crucial information about meaning in the context of words beyond mere word frequencies in tweets about suicide. By making data labeling more efficient, this work has enabled large-scale investigations on harmful and protective associations of social media content with suicide rates and help-seeking behavior.

Background
Returning universities to full on-campus operations while the coronavirus disease 2019 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts, and adoption of nonpharmaceutical intervention measures. Owing to the generalized academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight on which to base these decisions.

Methods
To address this problem, we analyzed a calibrated, data-driven agent-based simulation of transmission dynamics among 13 284 students and 1482 faculty members in a medium-sized European university. Wed use a colocation network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focused on actionable interventions that are part of the already existing decision process of universities to provide guidance for concrete policy decisions.

Results
Here we show that, with the Omicron variant of the severe acute respiratory syndrome coronavirus 2, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks, given the vaccination coverage of about 85% reported for students in Austria.

Conclusions
Our results show that controlling the spread of the virus with available vaccines in combination with nonpharmaceutical intervention measures is not feasible in the university setting if presence of students and faculty on campus is required.

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot (N = 3,433,554), AI (N = 2,821,614), automation (N = 879,092), bot (N = 21,559,939), intelligent agent (N = 15,119), and software agent (N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.

Low-carbon lifestyles are key to climate change mitigation, biodiversity conservation, and keeping the Earth in a safe operating space. Understanding the global feasibility and drivers of low-carbon lifestyles requires large scale data covering various countries, demographic and socioeconomic groups. In this study, we use the audience segmentation data from Facebook's advertising platform to analyse the extent and drivers of interest in sustainable lifestyles, plant-based diets in particular, at a global level. We show that formal education level is the most important factor affecting vegetarianism interest, and it creates a sharper difference in low-income countries. Gender is a strong distinguishing factor, followed by national gross domestic product per capita and age. These findings enable upscaling local empirical studies to a global level with confidence for integrated assessments of low-carbon lifestyles. Future studies can expand this analysis of social media audience data to other consumption areas, such as household energy demand, and can also contribute to quantifying the psychosocial drivers of low-carbon lifestyles, such as personal and social norms.

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.

En este artículo revisamos distintos modelos que usan herramientas de las ciencias de la complejidad para explicar fenómenos sociopolíticos y sugerimos que estos modelos deben ser interpretados como ‘modelos mínimos’ (Weisberg 2007; Batterman y Rice 2014). Concluimos que estos modelos altamente idealizados pueden no solo ayudarnos a distinguir los factores causales relevantes que dan origen a ciertos fenómenos sociopolíticos, sino que además en algunos casos pueden ayudarnos a visualizar políticas de intervención.

We investigated how people react emotionally to working with robots in three scenario-based role-playing survey experiments collected in 2019 and 2020 from the United States (Study 1: N = 1003; Study 2: N = 969, Study 3: N = 1059). Participants were randomly assigned to groups and asked to write a short post about a scenario in which we manipulated the number of robot teammates or the size of the social group (work team vs. organization). Emotional content of the corpora was measured using six sentiment analysis tools, and socio-demographic and other factors were assessed through survey questions and LIWC lexicons and further analyzed in Study 4. The results showed that people are less enthusiastic about working with robots than with humans. Our findings suggest these more negative reactions stem from feelings of oddity in an unusual situation and the lack of social interaction.

Objective:
The suicides of Kate Spade and Anthony Bourdain, two major American icons, in a span of days in June 2018 represent a unique and tragic natural experiment to characterize associations with actual suicides in the aftermath of celebrity suicides. The aim of this study was to identify changes in suicide counts after their deaths.

Methods:
Suicide data were obtained from the United States’ Centers for Disease Control and Prevention’s public-use mortality file. A time-series analysis was performed, examining monthly suicide data by age group (⩽19, 20–44, 45–64 and ⩾65 years), for both men and women, for all suicide methods and for hanging versus non-hanging methods, from January 1999 to December 2018. Seasonal autoregressive integrated moving-average models were fitted to the pre-June 2018 period, estimating suicides in subsequent months and identifying deviations from expected values. The volume of Twitter posts about Kate Spade and Anthony Bourdain was used as a proxy of societal attention.

Results:
Tweets about the celebrities were mainly concentrated in June 2018 and faded quickly in July. Total suicides exceeded the 95% confidence interval for June and approximated the upper limit of the 95% confidence interval in July. Over this 2-month span, there were 418 (95% confidence interval = [184, 652]) more suicides than expected, including 275 (95% confidence interval = [79, 471]) excess suicides in men and 182 (95% confidence interval = [93, 271]) in women. These equate to 4.8%, 4.1% and 9.1% increases above expected counts. There were 392 (95% confidence interval = [271, 514]) excess suicides by hanging, a 14.5% increase, with no significant increase in all other methods combined.

Conclusion and Relevance:
These findings demonstrate that mortality following celebrity suicides can occur at a similar magnitude to that observed for other public health emergencies. They underscore the urgency for interventions to mitigate imitation effects after celebrity suicide reporting.

In this article, the authors analyze citizens’ reactions to Brexit on social media after the referendum results by performing a content analysis of 5877 posts collected from the social media platform Flickr, written in English, German, French, Spanish or Italian. Their research aims to answer the three following questions: What multimodal practices are adopted by citizens when they react to societal events like Brexit? To what extent do these practices illustrate types of citizenship that are specific to social networks? Can we observe different reactions to Brexit according to the languages used by the citizens? The authors focus on the types of visual content the citizens used to react to Brexit, as well as on what types of social relations this content can particularly create between their authors and the other members of the Flick community. Their article also highlights to what extent these posts shared on Flickr show content that is in favour of, or against, Brexit.

The digitization of music has changed how we consume, produce, and distribute music. In this paper, we explore the effects of digitization and streaming on the globalization of popular music. While some argue that digitization has led to more diverse cultural markets, others consider that the increasing accessibility to international music would result in a globalized market where a few artists garner all the attention. We tackle this debate by looking at how cross-country diversity in music charts has evolved over 4 years in 39 countries. We analyze two large-scale datasets from Spotify, the most popular streaming platform at the moment, and iTunes, one of the pioneers in digital music distribution. Our analysis reveals an upward trend in music consumption diversity that started in 2017 and spans across platforms. There are now significantly more songs, artists, and record labels populating the top charts than just a few years ago, making national charts more diverse from a global perspective. Furthermore, this process started at the peaks of countries’ charts, where diversity increased at a faster pace than at their bases. We characterize these changes as a process of Cultural Divergence, in which countries are increasingly distinct in terms of the music populating their music charts.

Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors.

Background
The coronavirus (COVID-19) pandemic presents an unprecedented crisis with potential negative mental health impacts.

Methods
This study used data collected via Youper, a mental health app, from February through July 2020. Youper users (N = 157,213) in the United States self-reported positive and negative emotions and anxiety and depression symptoms during the pandemic. We examined emotions and symptoms before (pre), during (acute), and after (sustained) COVID-related stay-at-home orders.

Results
For changes in frequency of reported acute emotions, from the pre to acute periods, anxiety increased while tiredness, calmness, happiness, and optimism decreased. From the acute to sustained periods, sadness, depression, and gratitude increased. Anxiety, stress, and tiredness decreased. Between the pre and sustained periods, sadness and depression increased, as did happiness and calmness. Anxiety and stress decreased. Among symptom measures, anxiety increased initially, from the pre to the acute periods, but later returned to baseline.

Limitations
The study sample was primarily comprised of young people and women. The app does not collect racial or ethnicity data. These factors may limit generalizability. Sample size was also not consistent for all data collected.

Conclusions
The present study suggests that although there were initial negative impacts on emotions and mental health symptoms in the first few weeks, many Americans demonstrated resilience over the following months. The impact of the pandemic on mental health may not be as severe as predicted, although future work is necessary to understand longitudinal effects as the pandemic continues.

To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This allows decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We used web scraping and API access to retrieve data from the news platform derstandard.at, Twitter, and a chat platform for students. We documented the technical details of our workflow to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allowed us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We used special word clouds to visualize that overall difference. Longitudinally, our time series showed spikes in anxiety that can be linked to several events and media reporting. Additionally, we found a marked decrease in anger. The changes lasted for remarkably long periods of time (up to 12 weeks). We have also discussed these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at www.mpellert.at/covid19_monitor_austria/. Our work is part of a web archive of resources on COVID-19 collected by the Austrian National Library.

It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g., left vs right) and become increasingly polarized. We provide an agent-based model that reproduces alignment and polarization as emergent properties of opinion dynamics in a multi-dimensional space of continuous opinions. The mechanisms for the change of agents’ opinions in this multi-dimensional space are derived from cognitive dissonance theory and structural balance theory. We test assumptions from proximity voting and from directional voting regarding their ability to reproduce the expected emerging properties. We further study how the emotional involvement of agents, i.e., their individual resistance to change opinions, impacts the dynamics. We identify two regimes for the global and the individual alignment of opinions. If the affective involvement is high and shows a large variance across agents, this fosters the emergence of a dominant ideological dimension. Agents align their opinions along this dimension in opposite directions, i.e., create a state of polarization.

Polarization is threatening the stability of democratic societies. Until now, polarization research has focused on opinion extremeness, overlooking the correlation between different policy issues. In this paper, we explain the emergence of hyperpolarization, i.e., the combination of extremeness and correlation between issues, by developing a new theory of opinion formation called "Weighted Balance Theory (WBT)". WBT extends Heider's cognitive balance theory to encompass multiple weighted attitudes. We validated WBT on empirical data from the 2016 National Election Survey. Furthermore, we developed an opinion dynamics model based on WBT, which, for the first time, is able to generate hyperpolarization and to explain the link between affective and opinion polarization. Finally, our theory encompasses other phenomena of opinion dynamics, including mono-polarization and backfire effects.

We analyze an agent-based model to estimate how the costs and benefits of users in an online social network (OSN) impact the robustness of the OSN. Benefits are measured in terms of relative reputation that users receive from their followers. They can be increased by direct and indirect reciprocity in following each other, which leads to a core-periphery structure of the OSN. Costs relate to the effort to login, to maintain the profile, etc. and are assumed as constant for all users. The robustness of the OSN depends on the entry and exit of users over time. Intuitively, one would expect that higher costs lead to more users leaving and hence to a less robust OSN. We demonstrate that an optimal cost level exists, which maximizes both the performance of the OSN, measured by means of the long-term average benefit of its users, and the robustness of the OSN, measured by means of the lifetime of the core of the OSN. Our mathematical and computational analyses unfold how changes in the cost level impact reciprocity and subsequently the core-periphery structure of the OSN, to explain the optimal cost level.

It is well established that people often express emotions that are similar to those of other group members. However, people do not always express emotions that are similar to other group members, and the factors that determine when similarity occurs are not yet clear. In the current project, we examined whether certain situations activate specific emotional motives that influence the tendency to show emotional similarity. To test this possibility, we considered emotional responses to political situations that either called for weak (Studies 1 and 3) or strong (Study 2 and 4) negative emotions. Findings revealed that the motivation to feel weak emotions led people to be more influenced by weaker emotions than their own, whereas the motivation to feel strong emotions led people to be more influenced by stronger emotions than their own. Intriguingly, these motivations led people to change their emotions even after discovering that others’ emotions were similar to their initial emotional response. These findings are observed both in a lab task (Studies 1–3) and in real-life online interactions on Twitter (Study 4). Our findings enhance our ability to understand and predict emotional influence processes in different contexts and may therefore help explain how these processes unfold in group behavior.

Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.

When analyzing situations in which multiple people are experiencing emotions together—whether the emotions are positive or negative and whether the situations are online or offline—we are intuitively drawn to the emotions of each individual in the situation. However, this type of analysis often seems incomplete. In many of the cases in which people experience emotions together, there appear to be emergent macrolevel affective processes that cannot be readily captured at the individual level. In this article, we examine these macrolevel affective phenomena, which are termed collective emotions. We open with a general review of research on collective psychological processes. We then define collective emotions and discuss their key features. Next, we focus our attention on the emergent properties of collective emotions and map them using three dimensions: quality, magnitude, and time course. Finally, we discuss pressing open questions and future directions for research on collective emotions.

The dynamics of collaboration networks of firms follow a life cycle of growth and decline. That does not imply they also become less resilient. Instead, declining collaboration networks may still have the ability to mitigate shocks from firms leaving and to recover from these losses by adapting to new partners. To demonstrate this, we analyze 21.500 R&D collaborations of 14.500 firms in six different industrial sectors over 25 years. We calculate time-dependent probabilities of firms leaving the network and simulate drop-out cascades to determine the expected dynamics of decline. We then show that deviations from these expectations result from the adaptivity of the network, which mitigates the decline. These deviations can be used as a measure of network resilience.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. Only very recently has the question of “democratic backsliding” attracted any research attention. We argue that democratic instability is best understood with tools from complexity science. The explanatory power of complexity science arises from several features of complex systems. Their relevance in the context of democracy is discussed. Several policy recommendations are offered to help (re)stabilize current systems of representative democracy.

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and individual opinions in a nonlinear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

After collective traumas such as natural disasters and terrorist attacks, members of concerned communities experience intense emotions and talk profusely about them. Although these exchanges resemble simple emotional venting, Durkheim’s theory of collective effervescence postulates that these collective emotions lead to higher levels of solidarity in the affected community. We present the first large-scale test of this theory through the analysis of digital traces of 62,114 Twitter users after the Paris terrorist attacks of November 2015. We found a collective negative emotional response followed by a marked long-term increase in the use of lexical indicators related to solidarity. Expressions of social processes, prosocial behavior, and positive affect were higher in the months after the attacks for the individuals who participated to a higher degree in the collective emotion. Our findings support the conclusion that collective emotions after a disaster are associated with higher solidarity, revealing the social resilience of a community.

Privacy regulations for online platforms allow users to control their personal data. But what happens when our private attributes or behaviour can be inferred without our personal data? Researchers reveal that the behaviour of individuals is predictable using only the information provided by their friends in an online social network.

Background
Media recommendations for suicide reporting are recommended to prevent imitative suicide but little is known about social media reactions to different revelations about celebrity suicide.

Methods
Using the Twitter Application Programming Interface (API), we recorded public tweets mentioning Avicii from the day when his death was reported (N = 2,865,292). We compared that data with a dataset of random tweets. Furthermore, we recorded tweets including suicide in 124 languages before Avicii‘s death (N = 5,939,107). We processed English tweets mentioning Avicii with the Linguistic Inquiry and Word Count (LIWC) to quantify the frequency of affects and related linguistic signals. We also processed the text of tweets to detect tweets mentioning the suicide method, and we retrieved the list of followers of users who tweeted about the method. We constructed reply networks from the dataset, analysing three networks corresponding to the major news events about Avicii‘s death.

Results
Avicii's suicide sparked immediate strong interest with both positive (χ² = 781.06, p < 10⁻⁶) and negative emotional expressions (χ² = 1518.5, p < 10⁻⁶) in comparison to baseline levels. Subsequent revelations were associated with smaller peaks with mainly negative emotional content after Avicii's death was revealed as a suicide (χ² = 33.2, p < 10⁻⁶ and after news about the suicide method (χ² = 274.93, p < 10⁻⁶). Tweeting about the suicide method was infrequent, but twitter users who covered the method had more followers that users who did not (D = 0.1675, p < 10⁻⁶; t = 19.87, p < 10⁻⁶), and a noteworthy number of users had considerable exposure to the suicide method.

Limitations
This was a descriptive analysis.

Conclusions
Twitter users showed strong interest in news about Avicii's death and Avicii's suicide, but less so in the suicide method, and showed distinct tweeting behaviours based on the different revelations.

The idea that democracy is under threat, after being largely dormant for at least 40 years, is looming increasingly large in public discourse. Complex systems theory offers a range of powerful new tools to analyse the stability of social institutions in general, and democracy in particular. What makes a democracy stable? And which processes potentially lead to instability of a democratic system? This paper offers a complex systems perspective on this question, informed by areas of the mathematical, natural, and social sciences. We explain the meaning of the term 'stability' in different disciplines and discuss how laws, rules, and regulations, but also norms, conventions, and expectations are decisive for the stability of a social institution such as democracy.

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dc.contributor.author: Niederkrotenthaler, Thomas; Stack, Steven; Till, Benedikt; Sinyor, Mark; Pirkis, Jane; Rockett, Ian R. H.; Tran, Ulrich S.

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dc.contributor.author: Galaz, Victor; Daume, Stefan

Recent years have seen major political crises throughout the world, and foreign policy analysts nearly universally expect to see rising tensions within (and between) countries in the next 5–20 years. Being able to predict future crises and to assess the resilience of different countries to various shocks is of foremost importance in averting the potentially huge human costs of state collapse and civil war. The premise of this paper is that a transdisciplinary approach to forecasting social breakdown, recovery, and resilience is entirely feasible, as a result of recent breakthroughs in statistical analysis of large-scale historical data, the qualitative insights of historical and semiotic investigations, and agent-based models that translate between micro-dynamics of interacting individuals and the collective macro-level events emerging from these interactions. Our goal is to construct a series of probabilistic scenarios of social breakdown and recovery, based on historical crises and outcomes, which can aid the analysis of potential outcomes of future crises. We call this approach—similar in spirit to ensemble forecasting in weather prediction—multipath forecasting (MPF). This paper aims to set out the methodological premises and basic stages envisaged to realize this goal within a transdisciplinary research collaboration: first, the statistical analysis of a massive database of past instances of crisis to determine how actual outcomes (the severity of disruption and violence, the speed of resolution) depend on inputs (economic, political, and cultural factors); second, the encoding of these analytical insights into probabilistic, empirically informed computational models of societal breakdown and recovery—the MPF engine; third, testing the MPF engine to “predict” the trajectories and outcomes of another set of past social upheavals, which were not used in building the model. This “historical retrodiction” is an innovation that will allow us to further refine the MPF technology. Ultimately our vision is to use MPF to help write what we call “a history of possible futures,” in which the near- and medium-term paths of societies are probabilistically forecast.

Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service. We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet. Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.

We have conducted a content analysis of our exhaustive corpus of 5877 posts that contain the keyword ‘Brexit’, all published after the announcement of the Brexit referendum results on the social network Flickr. More specifically, this article homes in on the 67 posts in this corpus that mention the European Union, in order to qualitatively identify the multimodal discursive practices (combining text and image) used by citizens to express their concerns, or indeed fears, about Brexit in the context of the EU. Our analysis calls attention to the predominance of posts within the affective, rather than the utilitarian, paradigm of the European identity. In addition, the analysis of the images included in these posts highlights the particularly frequent metaphorization processes of everyday images.

Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media in particular are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 Billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access informational resources and help to narrow the economic gender gap.

Social networks affect individuals’ economic opportunities and well-being. However, few of the factors thought to shape networks—culture, language, education, and income—were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.

In this article we study to what extent the academic peer review process is influenced by social relations between the authors of a manuscript and the editor handling the manuscript. Taking the open access journal PlosOne as a case study, our analysis is based on a data set of more than 100,000 articles published between 2007 and 2015. Using available data on handling editor, submission and acceptance time of manuscripts, we study the question whether co-authorship relations between authors and the handling editor affect the manuscript handling time, i.e. the time taken between the submission and acceptance of a manuscript. Our analysis reveals (1) that editors handle papers co-authored by previous collaborators significantly more often than expected at random, and (2) that such prior co-author relations are significantly related to faster manuscript handling. Addressing the question whether these shorter manuscript handling times can be explained by the quality of publications, we study the number of citations and downloads which accepted papers eventually accumulate. Moreover, we consider the influence of additional (social) factors, such as the editor’s experience, the topical similarity between authors and editors, as well as reciprocal citation relations between authors and editors. Our findings show that, even when correcting for other factors like time, experience, and performance, prior co-authorship relations have a large and significant influence on manuscript handling times, speeding up the editorial decision on average by 19 days.

Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. This interest has led to the development of statistical models which are able to characterize the dynamics of threaded online conversations.

In this paper, we review research on statistical modeling of online discussions, in particular, we describe current generative models of the structure and growth of discussion threads. These are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms. We aim to provide a clear overview of the state of the art and to motivate future work in this relevant research field.

Sentiment analysis aims to automatically uncover the underlying attitude that we hold towards an entity. The aggregation of these sentiment over a population represents opinion polling and has numerous applications. Current text-based sentiment analysis rely on the construction of dictionaries and machine learning models that learn sentiment from large text corpora. Sentiment analysis from text is currently widely used for customer satisfaction assessment and brand perception analysis, among others. With the proliferation of social media, multimodal sentiment analysis is set to bring new opportunities with the arrival of complementary data streams for improving and going beyond text-based sentiment analysis. Since sentiment can be detected through affective traces it leaves, such as facial and vocal displays, multimodal sentiment analysis offers promising avenues for analyzing facial and vocal expressions in addition to the transcript or textual content. These approaches leverage emotion recognition and context inference to determine the underlying polarity and scope of an individual's sentiment. In this survey, we define sentiment and the problem of multimodal sentiment analysis and review recent developments in multimodal sentiment analysis in different domains, including spoken reviews, images, video blogs, human–machine and human–human interactions. Challenges and opportunities of this emerging field are also discussed leading to our thesis that multimodal sentiment analysis holds a significant untapped potential.

The pervasive presence of online media in our society has transferred a significant part of political deliberation to online forums and social networking sites. This article examines popularity, reputation, and social influence on Twitter using large-scale digital traces from 2009 to 2016. We process network information on more than 40 million users, calculating new global measures of reputation that build on the D-core decomposition and the bow-tie structure of the Twitter follower network. We integrate our measurements of popularity, reputation, and social influence to evaluate what keeps users active, what makes them more popular, and what determines their influence. We find that there is a range of values in which the risk of a user becoming inactive grows with popularity and reputation. Popularity in Twitter resembles a proportional growth process that is faster in its strongly connected component, and that can be accelerated by reputation when users are already popular. We find that social influence on Twitter is mainly related to popularity rather than reputation, but that this growth of influence with popularity is sublinear. The explanatory and predictive power of our method shows that global network metrics are better predictors of inactivity and social influence, calling for analyses that go beyond local metrics like the number of followers.

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

We compare data across 24,624 Twitter users to examine differences between entrepreneurs and the general population. Our analyses reveal that entrepreneurs manifest more positive and fewer negative emotions than the general population. Entrepreneurs also communicate more about work, and less about aspects related to personal life. Interestingly, during the early phases of a venture, positive emotions and work concerns increase, while negative emotions and life concerns decrease. Counterintuitively, work and negative emotions are negatively associated. Entrepreneurs express negative emotions 2.26 times less, and these negative emotions reduce by 8% after successful fundraising. Our work has implications for the understanding of work-life balance and of emotions in entrepreneurial contexts.

The Internet and social media have become increasingly important for children today, as they seek information, friends, and social support online. Social media is also home for communities that may advocate unhealthy behavior. Proanorexia (proana) and probulimia sites and online communities are publicly available; they are interactive and promote “thinspiration” (“inspirational” pictures of extremely thin bodies).1 Such material is easy to access, and some children might find it by accident. According to a 25-country EU Kids Online survey, for example, 10% of children aged 9 to 16 had seen eating disorder sites online, with girls being more commonly exposed to such material than boys.2 With the rapid expansion of social media, proana and other harmful online communities have a global audience. Proana communities are active on different social media sites, including Facebook, YouTube, Twitter, Instagram, Pinterest, Flickr, and Snapchat.

Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world’s knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation.

We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.

We study the changes in emotional states induced by reading and participating in online discussions, empirically testing a computational model of online emotional interaction. Using principles of dynamical systems, we quantify changes in valence and arousal through subjective reports, as recorded in three independent studies including 207 participants (110 female). In the context of online discussions, the dynamics of valence and arousal are composed of two forces: an internal relaxation towards baseline values independent of the emotional charge of the discussion, and a driving force of emotional states that depends on the content of the discussion. The dynamics of valence show the existence of positive and negative tendencies, while arousal increases when reading emotional content regardless of its polarity. The tendency of participants to take part in the discussion increases with positive arousal. When participating in an online discussion, the content of participants' expression depends on their valence, and their arousal significantly decreases afterwards as a regulation mechanism. We illustrate how these results allow the design of agent-based models to reproduce and analyze emotions in online communities. Our work empirically validates the microdynamics of a model of online collective emotions, bridging online data analysis with research in the laboratory.

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dc.contributor.author: Garas, Antonios; Schweitzer, Frank

Viral marketing seeks to maximize the spread of a campaign through an online social network, often targeting influential nodes with high centrality. In this article, we analyze behavioral aspects of influential users in trust-based product reviews communities, quantifying emotional expression, helpfulness, and user activity level. We focus on two independent product review communities, Dooyoo and Epinions, in which users can write product reviews and define trust links to filter product recommendations. Following the patterns of social contagion processes, we measure user social influence by means of the k-shell decomposition of trust networks. For each of these users, we apply sentiment analysis to extract their extent of positive, negative, and neutral emotional expression. In addition, we quantify the level of feedback they received in their reviews, the length of their contributions, and their level of activity over their lifetime in the community. We find that users of both communities exhibit a large heterogeneity of social influence, and that helpfulness votes and age are significantly better predictors of the influence of an individual than sentiment. The most active of the analyzed communities shows a particular structure, in which the inner core of users is qualitatively different from its periphery in terms of a stronger positive and negative emotional expression. These results suggest that both objective and subjective aspects of reviews are relevant to the communication of subjective experience.

The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.

Recent grassroots movements have suggested that online social networks might play a key role in their organization, as adherents have a fast, many-to-many, communication channel to help coordinate their mobilization. The structure and dynamics of the networks constructed from the digital traces of protesters have been analyzed to some extent recently. However, less effort has been devoted to the analysis of the semantic content of messages exchanged during the protest. Using the data obtained from a microblogging service during the brewing and active phases of the 15M movement in Spain, we perform the first large scale test of theories on collective emotions and social interaction in collective actions. Our findings show that activity and information cascades in the movement are larger in the presence of negative collective emotions and when users express themselves in terms related to social content. At the level of individual participants, our results show that their social integration in the movement, as measured through social network metrics, increases with their level of engagement and of expression of negativity. Our findings show that non-rational factors play a role in the formation and activity of social movements through online media, having important consequences for viral spreading.

Background: Pro-anorexia communities exist online and encourage harmful weight loss and weight control practices, often through emotional content that enforces social ties within these communities. User-generated responses to videos that directly oppose pro-anorexia communities have not yet been researched in depth.
Objective: The aim was to study emotional reactions to pro-anorexia and anti-pro-anorexia online content on YouTube using sentiment analysis.
Methods: Using the 50 most popular YouTube pro-anorexia and anti-pro-anorexia user channels as a starting point, we gathered data on users, their videos, and their commentators. A total of 395 anorexia videos and 12,161 comments were analyzed using positive and negative sentiments and ratings submitted by the viewers of the videos. The emotional information was automatically extracted with an automatic sentiment detection tool whose reliability was tested with human coders. Ordinary least squares regression models were used to estimate the strength of sentiments. The models controlled for the number of video views and comments, number of months the video had been on YouTube, duration of the video, uploader’s activity as a video commentator, and uploader’s physical location by country.
Results: The 395 videos had more than 6 million views and comments by almost 8000 users. Anti-pro-anorexia video comments expressed more positive sentiments on a scale of 1 to 5 (adjusted prediction [AP] 2.15, 95% CI 2.11-2.19) than did those of pro-anorexia videos (AP 2.02, 95% CI 1.98-2.06). Anti-pro-anorexia videos also received more likes (AP 181.02, 95% CI 155.19-206.85) than pro-anorexia videos (AP 31.22, 95% CI 31.22-37.81). Negative sentiments and video dislikes were equally distributed in responses to both pro-anorexia and anti-pro-anorexia videos.
Conclusions: Despite pro-anorexia content being widespread on YouTube, videos promoting help for anorexia and opposing the pro-anorexia community were more popular, gaining more positive feedback and comments than pro-anorexia videos. Thus, the anti-pro-anorexia content provided a user-generated counterforce against pro-anorexia content on YouTube. Professionals working with young people should be aware of the social media dynamics and versatility of user-generated eating disorder content online.

Online social media has recently irrupted as the last major venue for the propagation of news and cultural content, competing with traditional mass media and allowing citizens to access new sources of information. In this paper, we study collectively filtered news and popular content in Twitter, known as Trending Topics (TTs), to quantify the extent to which they show similar biases known for mass media. We use two datasets collected in 2013 and 2014, including more than 300.000 TTs from 62 countries. The existing patterns of leader-follower relationships among countries reveal systemic biases known for mass media: Countries concentrate their attention to small groups of other countries, generating a pattern of centralization in which TTs follow the gradient of wealth across countries. At the same time, we find subjective biases within language communities linked to the cultural similarity of countries, in which countries with closer cultures and shared languages tend to follow each other’s TTs. Moreover, using a novel methodology based on the Google News service, we study the influence of mass media in TTs for four countries. We find that roughly half of the TTs in Twitter overlap with news reported by mass media, and that the rest of TTs are more likely to spread internationally within Twitter. Our results confirm that online social media have the power to independently spread content beyond mass media, but at the same time social media content follows economic incentives and is subject to cultural factors and language barriers.

Political polarization is traditionally analyzed through the ideological stances of groups and parties, but it also has a behavioral component that manifests in the interactions between individuals. We present an empirical analysis of the digital traces of politicians in politnetz.ch, a Swiss online platform focused on political activity, in which politicians interact by creating support links, comments, and likes. We analyze network polarization as the level of intra-party cohesion with respect to inter-party connectivity, finding that supports show a very strongly polarized structure with respect to party alignment. The analysis of this multiplex network shows that each layer of interaction contains relevant information, where comment groups follow topics related to Swiss politics. Our analysis reveals that polarization in the layer of likes evolves in time, increasing close to the federal elections of 2011. Furthermore, we analyze the internal social network of each party through metrics related to hierarchical structures, information efficiency, and social resilience. Our results suggest that the online social structure of a party is related to its ideology, and reveal that the degree of connectivity across two parties increases when they are close in the ideological space of a multi-party system.

Building relationships is crucial for satisfaction and success, especially when entering new social contexts. In the present paper, we investigate whether attempting to improve others’ feelings helps people to make connections in new networks. In Study 1, a social network study following new networks of people for a 12-week period indicated that use of interpersonal emotion regulation (IER) strategies predicted growth in popularity, as indicated by other network members’ reports of spending time with the person, in work and non-work interactions. In Study 2, linguistic analysis of the tweets from over 8000 Twitter users from formation of their accounts revealed that use of IER predicted greater popularity in terms of the number of followers gained. However, not all types of IER had positive effects. Behavioral IER strategies (which use behavior to reassure or comfort in order to regulate affect) were associated with greater popularity, while cognitive strategies (which change a person’s thoughts about his or her situation or feelings in order to regulate affect) were negatively associated with popularity. Our findings have implications for our understanding of how new relationships are formed, highlighting the important the role played by intentional emotion regulatory processes.

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.

Affect Listeners are applied as tools for studying the role of emotions in online communication. They need to interact both in dyads as well as in group settings with multiple users. In this paper, we present the evolution of such affective dialog systems from a focus on dyadic interaction to multi-party interaction on chat networks. Starting from experiments on the use of these dialog systems in virtual dyadic settings, we outline the requirements, design and implementation decisions necessary to apply the systems to affective interactions with multiple users. Finally, we introduce two realisations of Interactive Affective Bots designed for such interaction scenarios that integrate modelling of individuals and groups as part of their decision mechanism.

Measuring culture and its dynamics through surveys has important limitations, but the emerging field of computational social science allows us to overcome them by analyzing large-scale datasets. In this article, we study cultural dynamics through the votes in the Eurovision song contest, which are decided by a crowd-based scheme in which viewers vote through mobile phone messages. Taking into account asymmetries and imperfect perception of culture, we measure cultural relations among European countries in terms of cultural affinity. We propose the Friend-or-Foe coefficient, a metric to measure voting biases among participants of a Eurovision contest. We validate how this metric represents cultural affinity through its relation with known cultural distances, and through numerical analysis of biased Eurovision contests. We apply this metric to the historical set of Eurovision contests from 1975 to 2012, finding new patterns of stronger modularity than using votes alone. Furthermore, we define a measure of polarization that, when applied to empirical data, shows a sharp increase within EU countries during 2010 and 2011. We empirically validate the relation between this polarization and economic indicators in the EU, showing how political decisions influence both the economy and the way citizens relate to the culture of other EU members.

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional "tone" of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.

We show that the frequency of word use is not only determined by the word length [1] and the average information content [2], but also by its emotional content. We have analyzed three established lexica of affective word usage in English, German, and Spanish, to verify that these lexica have a neutral, unbiased, emotional content. Taking into account the frequency of word usage, we find that words with a positive emotional content are more frequently used. This lends support to Pollyanna hypothesis [3] that there should be a positive bias in human expression. We also find that negative words contain more information than positive words, as the informativeness of a word increases uniformly with its valence decrease. Our findings support earlier conjectures about (i) the relation between word frequency and information content, and (ii) the impact of positive emotions on communication and social links.

Emotions are an important part of most societal dynamics. As with face to face meetings, Internet exchanges may not only include factual information but may also elicit emotional responses; how participants feel about the subject discussed or other group members. The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. We present results of two years of studies performed in the EU Large Scale Integrating Project CYBEREMOTIONS (Collective emotions in cyberspace) Our goal is to understand the role of collective emotions in creating, forming and breaking-up ICT mediated communities and to prepare the background for the next generation of emotionally-intelligent ICT services. Project results have already attracted a lot of attention from various mass media and research journals including the Science and New Scientist magazines. Nine Project teams are organised in three layers (data, theory and ICT output).

We develop an agent-based framework to model the emergence of collective emotions, which is applied to online communities. Agent’s individual emotions are described by their valence and arousal. Using the concept of Brownian agents, these variables change according to a stochastic dynamics, which also considers the feedback from online communication. Agents generate emotional information, which is stored and distributed in a field modeling the online medium. This field affects the emotional states of agents in a non-linear manner. We derive conditions for the emergence of collective emotions, observable in a bimodal valence distribution. Dependent on a saturated or a superlinear feedback between the information field and the agent’s arousal, we further identify scenarios where collective emotions only appear once or in a repeated manner. The analytical results are illustrated by agent-based computer simulations. Our framework provides testable hypotheses about the emergence of collective emotions, which can be verified by data from online communities.