Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.Comment: In the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Please cite the conference versio

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events

Evolution of Online User Behavior During a Social Upheaval

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.Comment: Best Paper Award at ACM Web Science 201

Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

Political Magazines on Twitter during Election 2012: Framing, Uniting, Dividing

This study offers a content analysis of Twitter activity from 16 American political opinion magazines during the month before the 2012 presidential election. The study is an exploratory attempt to operationalize aspects of tweets that may contribute to frame alignment processes and mobilization among Twitter users. The analysis identifies these components and examines how political magazines’ Twitter activity may demonstrate aspects of this process. These magazines must consider both the normative goal of achieving specific political gains by mobilizing readers and the pragmatic goal of remaining sustainable as publishing enterprises. The degree to which their Twitter usage reflects frame alignment processes may not only reinforce political mobilization, but also affect the longevity of their publications. This analysis offers practical and theoretical insights into the changing role of political magazines in an increasingly digital era of political engagement

#MeToo as Catalyst: A Glimpse into 21st Century Activism

The Twitter hashtag #MeToo has provided an accessible medium for users to share their personal experiences and make public the prevalence of sexual harassment, assault, and violence against women. This online phenomenon, which has largely involved posting on Twitter and “retweeting” to share other’s posts has revealed crucial information about the scope and nature of sexual harassment and misconduct. More specifically, social media has served as a central forum for this unprecedented global conversation, where previously silenced voices have been amplified, supporters around the world have been united, and resistance has gained steam. This Essay discusses the #MeToo movement within the broader context of social media activism, explaining how this unique form of collective action is rapidly evolving. We offer empirical insights into the types of conversations taking place under the hashtag and the extent to which the movement is leading to broader social change. While it is unclear which changes are sustainable over time, it is clear that the hashtag #MeToo has converted an online phenomenon into tangible change, sparking legal, political, and social changes in the short run. This Essay provides data to illustrate some of these changes, which demonstrate how posting online can serve as an impetus, momentum, and legitimacy for broader movement activity and changes offline more characteristic of traditional movement strategies

#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.Comment: This is a preprint of an article appearing at ICWSM 201

Sharing news, making sense, saying thanks: patterns of talk on Twitter during the Queensland floods

Abstract: This paper examines the discursive aspects of Twitter communication during the floods in the summer of 2010–2011 in Queensland, Australia. Using a representative sample of communication associated with the #qldfloods hashtag on Twitter, we coded and analysed the patterns of communication. We focus on key phenomena in the use of social media in crisis communication: communal sense-making practices, the negotiation of participant roles, and digital convergence around shared events. Social media is used both as a crisis communication and emergency management tool, as well as a space for participants to engage in emotional exchanges and communication of distress.Authored by Frances Shaw, Jean Burgess, Kate Crawford and Axel Bruns