This paper explores the nature and spread of viral WhatsApp content among everyday users in three diverse countries: India, Indonesia, and Colombia. By analyzing hundreds of viral messages collected with participants’ consent from private WhatsApp groups, we provide one of the first cross-cultural categorizations of viral content on WhatsApp. Despite the differences in cultural and geographic settings, our findings reveal striking similarities in the types of groups users engage with and the viral content they receive, particularly in the prevalence of misinformation. Our comparative analysis shows that viral content often includes political and religious narratives, with misinformation frequently recirculated despite prior debunking by fact-checking organizations. These parallels suggest that closed messaging platforms like WhatsApp facilitate similar patterns of information dissemination across different cultural contexts. This work contributes to the broader understanding of global digital communication ecosystems and provides a foundation for future research on information flow and moderation strategies in private messaging platforms.

The paper presents a comprehensive analysis of hate speech and trolling campaigns on Facebook during the 2016 national elections in the Philippines. Employing a vast dataset of hundreds of millions of Facebook comments, we uncover the first empirical evidence of coordinated online hate speech campaigns in this political context. Our findings reveal that over 12% of comments on political pages contained hate speech, predominantly originating from supporters of then-candidate Rodrigo Duterte and his affiliates. We further examine the relationship between offline political events and online hate speech, finding a surge in hateful commenting following the launch of Duterte’s campaign, though similar spikes were not observed after some of his later controversial remarks. Alarmingly, we observe a “spillover effect”: regular social media users, after exposure to orchestrated hate speech by highly active “troll” accounts, began emulating these behaviors. This contagion effect highlights a worrying trend in which hate speech normalizes and spreads within online communities. Overall, our results shed light on the dynamics of digital political campaigns and their implications for democracy and public discourse. Given Facebook’s ubiquity in the Philippines, these findings raise significant concerns about social media’s influence on electoral politics and the health of online civic dialogue.

The relationship between television shows and social media has become increasingly intertwined in recent years. Social media platforms, particularly Twitter, have emerged as significant sources of public opinion and discourse on topics discussed in television shows. In India, news debates leverage the popularity of social media to promote hashtags and engage users in discussions and debates on a daily basis. This paper focuses on the analysis of one of India's most prominent and widely-watched TV news debate shows: 'Arnab Goswami -- The Debate'. The study examines the content of the show by analyzing the hashtags used to promote it and the social media data corresponding to these hashtags. The goal is to understand the composition of the audience engaged in social media discussions related to the show. The findings reveal that the show exhibits a strong bias towards the ruling Bharatiya Janata Party (BJP), with over 60% of the debates featuring either pro-BJP or anti-opposition content. Social media support for the show primarily comes from BJP supporters. Notably, BJP leaders and influencers play a significant role in promoting the show on social media, leveraging their existing networks and resources to artificially trend specific hashtags. Furthermore, the study uncovers a reciprocal flow of information between the TV show and social media. We find evidence that the show's choice of topics is linked to social media posts made by party workers, suggesting a dynamic interplay between traditional media and online platforms. By exploring the complex interaction between television debates and social media support, this study contributes to a deeper understanding of the evolving relationship between these two domains in the digital age. The findings hold implications for media researchers and practitioners, offering insights into the ways in which social media can influence traditional media and vice versa.

In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates [27]. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of a prime-time television debate show in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization [18]. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. To catalyze further research in this area, we also release the code, dataset collected and supplemental pdf.1

Misinformation is a global challenge, but successful mitigations must come from the communities affected and not be imposed by external entities. Co·Insights is a multi-year NSF-funded initiative building capacity to respond to misinformation and harmful narratives in Asian American and Pacific Islander (AAPI) communities, based on a deep involvement with grassroots organizations and a co-construction of tools grounded on community needs. Co·Insights' unique cross-sectoral, cross-disciplinary collaboration is a convergence of information retrieval, computational social science, and ethnographic inquiry with a unique platform that enables community organizations, fact-checkers, and academics to work together to respond effectively to harmful content targeting communities. In this SIRIP talk designed for a technical audience, we will share lessons learned from the first 2.5 years of Co·Insights, and how we are bridging the academic--praxis divide by integrating state-of-the-art research developments into large-scale deployed systems. Topics covered will include: the challenges and joys of collaborating across disciplinary and sectoral boundaries; community-driven approaches that utilize information retrieval techniques such as claim matching in concert with emerging best practices in misinformation mitigation; open problems we have encountered in the space; and future directions we find promising. Co·Insights is led by Meedan, a global non-profit providing award-winning software solutions to mitigate misinformation; and in partnership with AAPI community organizations and several academic institutions.

Timeline algorithms are key parts of online social networks, but during recent years they have been blamed for increasing polarization and disagreement in our society. Opinion-dynamics models have been used to study a variety of phenomena in online social networks, but an open question remains on how thesemodels can be augmented to take into account the fine-grained impact of user-level timeline algorithms. We make progress on this question by providing a way to model the impact of timeline algorithms on opinion dynamics. Specifically, we show how the popular Friedkin--Johnsen opinion-formation model can be augmented based on aggregate information, extracted from timeline data. We use our model to study the problem of minimizing the polarization and disagreement; we assume that we are allowed to make small changes to the users' timeline compositions by strengthening some topics of discussion and penalizing some others. We present a gradient descent-based algorithm for this problem, and show that under realistic parameter settings, our algorithm computes a (1+\varepsilon)-approximate solution in time~\tO(m\sqrtn łg(1/\varepsilon)), where m~is the number of edges in the graph and n~is the number of vertices. We also present an algorithm that provably computes an \varepsilon-approximation of our model in near-linear time. We evaluate our method on real-world data and show that it effectively reduces the polarization and disagreement in the network. Finally, we release an anonymized graph dataset with ground-truth opinions and more than 27\,000~nodes (the previously largest publicly available dataset contains less than 550~nodes).

In addition to more personalized content feeds, some leading social media platforms give a prominent role to content that is more widely popular. On Twitter, "trending topics" identify popular topics of conversation on the platform, thereby promoting popular content which users might not have otherwise seen through their network. Hence, "trending topics" potentially play important roles in influencing the topics users engage with on a particular day. Using two carefully constructed data sets from India and Turkey, we study the effects of a hashtag appearing on the trending topics page on the number of tweets produced with that hashtag. We specifically aim to answer the question: How many new tweeting using that hashtag appear because a hashtag is labeled as trending? We distinguish the effects of the trending topics page from network exposure and find there is a statistically significant, but modest, return to a hashtag being featured on trending topics. Analysis of the types of users impacted by trending topics shows that the feature helps less popular and new users to discover and spread content outside their network, which they otherwise might not have been able to do.

WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, understanding misbehavior on WhatsApp is an important issue. The sending of unwanted junk messages by unknown contacts via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. We address this gap by studying junk messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp groups in India. We characterise both junk content and senders. We find that nearly 1 in 10 messages is unwanted content sent by junk senders, and a number of unique strategies are employed to reflect challenges faced on WhatsApp, e.g., the need to change phone numbers regularly. We finally experiment with on-device classification to automate the detection of junk, whilst respecting end-to-end encryption.

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such ‘fear speech’ messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in. We have made our code and dataset public for other researchers.

The worldwide spread of COVID-19 has prompted extensive online discussions, creating an ‘infodemic’ on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands of messages containing text and images, we identify the main categories of discussion. We focus on COVID-19 messages and understand the different types of images/text messages being propagated. By exploring user behavior related to COVID messages, we inspect how misinformation is spread. Finally, by quantifying the flow of information across WhatsApp and Twitter, we show how information spreads across platforms and how WhatsApp acts as a source for much of the information shared on Twitter.

Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting products via their social media feeds. We gather a large-scale Instagram dataset covering thousands of accounts advertising products, and create a categorisation based on the number of users they reach. We then provide a detailed analysis of the types of products being advertised by these accounts, their potential reach, and the engagement they receive from their followers. Based on our findings, we train machine learning models to distinguish sponsored content from non-sponsored, and identify cases where people are generating sponsored posts without labelling them.

People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users» existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem.

Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.

In this thesis, we develop methods to (i) detect and quantify the existence of filter bubbles in social media, (ii) monitor their evolution over time, and finally, (iii) devise methods to overcome the effects caused by filter bubbles. We are the first to propose an end-to-end system that solves the problem of filter bubbles completely algorithmically. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.

Society is often polarized by controversial issues that split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of `echo chambers', i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph, which is measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph.

Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content.

We propose a new scalable algorithm for the facility-location problem. We study the graph setting, where the cost of serving a client from a facility is represented by the shortest-path distance on a graph. This setting is applicable to various problems arising in the Web and social media, and allows to leverage the inherent sparsity of such graphs.

By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube. We study 87K Twitter users, 5.6 million YouTube videos and 15 million video sharing events from user-, video- and sharing-event-centric perspectives. We show that features of Twitter users correlate with YouTube features and sharing-related features. For example, urban users are quicker to share than rural users. We find a superlinear relationship between initial Twitter shares and the final amounts of views. We discover that Twitter activity metrics play more role in video popularity than mere amount of followers. We also reveal the existence of correlated behavior concerning the time between video creation and sharing within certain timescales, showing the time onset for a coherent response, and the time limit after which collective responses are extremely unlikely. Response times depend on the category of the video, suggesting Twitter video sharing is highly dependent on the video content. To the best of our knowledge, this is the first large-scale study combining YouTube and Twitter data, and it reveals novel, detailed insights into who watches (and shares) what on YouTube, and when.

We present a demo that shows Twitter hashtag usage in Egypt from the angle of Islamist vs. secular polarization. The demo not only provides insights about current events in Egypt, but also reveals differences in attitudes of the two political camps with respect to major events abroad. The demo is publicly accessible at http://sc1.qcri.org/twitter/egypt/weekly/.

We use public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter. Starting with a set of prominent seed Twitter users from both camps, we follow retweeting edges to obtain an extended network of users with inferred political orientation. We present an in-depth description of the members of the two camps, both in terms of behavior on Twitter and in terms of offline characteristics such as gender. Through the identification of partisan users, we compute a valence on the secular vs. Islamist axis for hashtags and use this information both to analyze topical interests and to quantify how polarized society as a whole is at a given point in time. For the last 12 months, large values on this "polarization barometer" coincided with periods of violence. Tweets are furthermore annotated using hand-crafted dictionaries to quantify the usage of (i) religious terms, (ii) derogatory terms referring to other religions, and (ii) references to charitable acts. The combination of all the information allows us to test and quantify a number of stereo-typical hypotheses such as (i) that religiosity and political Islamism are correlated, (ii) that political Islamism and negative views on other religions are linked, (iii) that religiosity goes hand in hand with charitable giving, and (iv) that the followers of the Egyptian Muslim Brotherhood are more tightly connected and expressing themselves "in unison" than the secular opposition. Whereas a lot of existing literature on the Arab Spring and the Egyptian Revolution is largely of qualitative and descriptive nature, our contribution lies in providing a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.

We present Political Search Trends, a browser based web search analysis tool that (i) assigns a political leaning to web search queries, (ii) detects trending political queries in a given week, and (iii) links search queries to fact-checked statements. In terms of methodology, it showcases the power of analyzing queries leading to clicks on selected, annotated web sites of interest.