Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis

A Relational Investigation of Political Polarization on Twitter

Over the last several decades there has been a debate among social scientists on whether the United States has become, or is in the process of being, politically polarized. These conversations started with discussion of the “culture wars,” moved to the discussion of selective exposure and media outrage, and currently involve concerns about online radicalization and the spread of online misinformation. Throughout these themes one characteristic has remained constant: a lack of systematic evidence despite anecdotes and feelings of animosity between the two parties. Today researchers are beginning to shift from operationalizing political polarization as growing divides in attitudes towards policy issues towards a focus on political animosity. Scholars attempting to understand the origins of affective polarization have looked at the effect of political identity, out-group perceptions, and the diffusion of moral and emotional content in social media networks. In the current study I build on this literature using a panel of longitudinal data Twitter users to examine whether there is an association between following prominent partisan Twitter accounts and the expression of emotional valence through Tweeting or Retweeting. I take a relational approach to analysis by examining how this relationship varies between networks of Twitter users and under different historical circumstances. I argue that this relational approach is necessary for understanding how political polarization is unfolding in the country and that the lack of a relational approach may explain why political polarization has been downplayed in systematic studies. This study finds that the amount of political polarization on Twitter is dependent both on cultural and historical context. It makes contributions to the literature on political polarization in the United States, research methodology, and has implications for reducing radicalization in online spaces

Managing Diverse Online Networks in the Context of Polarization:Understanding how we grow apart on and through social media

Social media enable their users to be connected with a diverse group of people increasing their chances of coming across divergent viewpoints. Thus, network diversity is a key issue for understanding the potentials of social media for creating a cross-cutting communication space that is one of the premises of a functioning democracy. This article analyzes the strategies social media users adopt to manage their network diversity in the context of increasing polarization. The study is based on 29 semi-structured interviews with diverse social media users from Turkey and qualitative network maps. Furthermore, the study adopts a cross-platform approach comparing Facebook, Twitter, and WhatsApp in relation to the diversity of their users’ networks. The study shows that social media users adopt different strategies interchangeably in specific contexts. These strategies include visible (unfriending, blocking) and invisible (muting, unfollowing, and ignoring) forms of disconnection, debating, observing divergent opinions, and self-censorship. Political interest of social media users, political climate, issue sensitivity, and “imagined affordances” of social media platforms play a role in users’ choices about which strategy to choose when they are confronted with divergent viewpoints through their diverse online networks. Building on the unfriending literature that points out to rather partisan users, who unfriend, unfollow or block others, this article demonstrates that in peak moments of polarization, also the politically disengaged or moderate users disconnect from diverse others

What about mood swings? Identifying depression on Twitter with temporal measures of emotions

Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task

URL-BERT: Training Webpage Representations via Social Media Engagements

Understanding and representing webpages is crucial to online social networks where users may share and engage with URLs. Common language model (LM) encoders such as BERT can be used to understand and represent the textual content of webpages. However, these representations may not model thematic information of web domains and URLs or accurately capture their appeal to social media users. In this work, we introduce a new pre-training objective that can be used to adapt LMs to understand URLs and webpages. Our proposed framework consists of two steps: (1) scalable graph embeddings to learn shallow representations of URLs based on user engagement on social media and (2) a contrastive objective that aligns LM representations with the aforementioned graph-based representation. We apply our framework to the multilingual version of BERT to obtain the model URL-BERT. We experimentally demonstrate that our continued pre-training approach improves webpage understanding on a variety of tasks and Twitter internal and external benchmarks

Mind Economy: Dynamic Graph Analysis of Communications

Social networks are growing in reach and impact but little is known about their structure, dynamics, or users’ behaviors. New techniques and approaches are needed to study and understand why these networks attract users’ persistent attention, and how the networks evolve. This thesis investigates questions that arise when modeling human behavior in social networks, and its main contributions are: • an infrastructure and methodology for understanding communication on graphs; • identification and exploration of sub-communities; • metrics for identifying effective communicators in dynamic graphs; • a new definition of dynamic, reciprocal social capital and its iterative computation • a methodology to study influence in social networks in detail, using • a class hierarchy established by social capital • simulations mixed with reality across time and capital classes • various attachment strategies, e.g. via friends-of-friends or full utility optimization • a framework for answering questions such as “are these influentials accidental” • discovery of the “middle class” of social networks, which as shown with our new metrics and simulations is the real influential in many processes Our methods have already lead to the discovery of “mind economies” within Twitter, where interactions are designed to increase ratings as well as promoting topics of interest and whole subgroups. Reciprocal social capital metrics identify the “middle class” of Twitter which does most of the “long-term” talking, carrying the bulk of the system-sustaining conversations. We show that this middle class wields the most of the actual influence we should care about — these are not “accidental influentials.” Our approach is of interest to computer scientists, social scientists, economists, marketers, recruiters, and social media builders who want to find and present new ways of exploring, browsing, analyzing, and sustaining online social networks

Using mixed methods to track the growth of the Web: tracing open government data initiatives

In recent years, there have been a rising number of Open Government Data (OGD) initiatives; a political, social and technical movement armed with a common goal of publishing government data in open, re-usable formats in order to improve citizen-to-government transparency, efficiency, and democracy. As a sign of commitment, the Open Government Partnership was formed, comprising of a collection of countries striving to achieve OGD. Since its initial launch, the number of countries committed to adopting an Open Government Data agenda has grown to more than 50; including countries from South America to the Far East.Current approaches to understanding Web initiatives such as OGD are still being developed. Methodologies grounded in multidisciplinarity are still yet to be achieved; typically research follows a social or technological approach underpinned by quantitative or qualitative methods, and rarely combining the two into a single analytical framework. In this paper, a mixed methods approach will be introduced, which uses qualitative data underpinned by sociological theory to complement a quantitative analysis using computer science techniques. This method aims to provide an alternative approach to understanding the socio-technical activities of the Web. To demonstrate this, the activities of the UK Open Government Data initiative will be explored using a range of quantitative and qualitative data, examining the activities of the community, to provide a rich analysis of the formation and development of the UK OGD community

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area