bluuebunny/crossref_metadata_2025 huggingface dataset:
Language Matters In Twitter: A Large Scale Study
Authors: Hong Lichan , Convertino Gregorio , Chi Ed
Published: November 2022

Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51%). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39% of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools.
A Bootstrapping Approach to Identifying Relevant Tweets for Social TV
Authors: Dan Ovidiu , Feng Junlan , Davison Brian
Published: November 2022

Manufacturers of TV sets have recently started adding social media features to their products. Some of these products display microblogging messages relevant to the TV show which the user is currently watching. However, such systems suffer from low precision and recall when they use the title of the show to search for relevant messages. Titles of some popular shows such as Lost or Survivor are highly ambiguous, resulting in messages unrelated to the show. Thus, there is a need to develop filtering algorithms that can achieve both high precision and recall. Filtering microblogging messages for Social TV poses several challenges, including lack of training data, lack of proper grammar and capitalization, lack of context due to text sparsity, etc. We describe a bootstrapping algorithm which uses a small manually labeled dataset, a large dataset of unlabeled messages, and some domain knowledge to derive a high precision classifier that can successfully filter microblogging messages which discuss television shows. The classifier is designed to generalize to TV shows which were not part of the training set. The algorithm achieves high precision on our two test datasets and successfully generalizes to unseen television shows. Furthermore, it compares favorably to a text classifier specifically trained on the television shows used for testing.
Tutorials
Authors: Breslin John
Published: November 2022

The ICWSM 2012 conference tutorials will be How to Analyze Massive Social Network Datasets without a Cluster, presented by Derek Ruths; Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL, presented by Marc Smith; Evidenced-Based Social Design of Online Communities: Getting to Critical Mass and Encouraging Contributions, presented by Paul Resnick and Robert Kraut; Sentiment Mining from User Generated Content, presented by Lyle Ungar and Ronen Feldman; and Information Extraction for Social Media Anaylsis, presented by Denilson Barbosa.
Modeling Polarizing Topics: When Do Different Political Communities Respond Differently to the Same News?
Authors: Balasubramanyan Ramnath , Cohen William , Pierce Douglas , Redlawsk David
Published: November 2022

Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multitarget, semisupervised latent variable model, MCR-LDA to model this process by analyzing political blogs posts and their comment sections from different political communities jointly to predict the degree of polarization that news topics cause. Inspecting the model after inference reveals topics and the degree to which it triggers polarization. In this approach, community responses to news topics are observed using sentiment polarity and comment volume which serves as a proxy for the level of interest in the topic. In this context, we also present computational methods to assign sentiment polarity to the comments which serve as targets for latent variable models that predict the polarity based on the topics in the blog content. Our results show that the joint modeling of communities with different political beliefs using MCR-LDA does not sacrifice accuracy in sentiment polarity prediction when compared to approaches that are tailored to specific communities and additionally provides a view of the polarization in responses from the different communities.
Temporal Motifs Reveal the Dynamics of Editor Interactions in Wikipedia
Authors: Jurgens David , Lu Tsai-Ching
Published: November 2022

Wikipedia is a collaborative setting with both combative and cooperative editing. We propose a new method for investigating the types of editor interactions using a novel representation of Wikipedia's revision history as a temporal, bipartite network with multiple node and edge types for users and revisions. From this representation we identify significant author interactions as network motifs and show how the motif types capture important, diverse editing behaviors. Two experiments demonstrate the further benefit of motifs. First, we demonstrate significant performance improvement over a purely revision-based analysis in classifying pages as combative or cooperative page by using motifs; and second we use motifs as a basis for analyzing trends in the dynamics of editor behavior to explain Wikipedia's content growth.
The YouTube Social Network
Authors: Wattenhofer Mirjam , Wattenhofer Roger , Zhu Zack
Published: November 2022

Today, YouTube is the largest user-driven video content provider in the world; it has become a major platform for disseminating multimedia information. A major contribution to its success comes from the user-to-user social experience that differentiates it from traditional content broadcasters. This work examines the social network aspect of YouTube by measuring the full-scale YouTube subscription graph, comment graph, and video content corpus. We find YouTube to deviate significantly from network characteristics that mark traditional online social networks, such as homophily, reciprocative linking, and assortativity. However, comparing to reported characteristics of another content-driven online social network, Twitter, YouTube is remarkably similar. Examining the social and content facets of user popularity, we find a stronger correlation between a user's social popularity and his/her most popular content as opposed to typical content popularity. Finally, we demonstrate an application of our measurements for classifying YouTube Partners, who are selected users that share YouTube's advertisement revenue. Results are motivating despite the highly imbalanced nature of the classification problem.
Event Diffusion Patterns in Social Media
Authors: Kim Minkyoung , Xie Lexing , Christen Peter
Published: November 2022

This study focuses on real-world events and their reflections on the continuous stream of online discussions. Studying event diffusion on social media is important, as this will tell us how a significant event (such as a natural disaster) spreads and evolves interacting with other events, and who has helped spreading the event. Tracking an ever-changing list of often unanticipated events is difficult, and most prior work has focused on specific event derivatives such as quotes or user-generated tags. In this paper, we propose a method for identifying real-world events on social media, and present observations about event diffusion patterns across diverse media types such as news, blogs, and social networking sites. We first construct an event registry based on the Wikipedia portal of global news events, and we represent each real-world event with entities that embody the 5W1H (e.g., organization, person name, place) used in news coverage. We then label each web document with the list of identified events based on entity similarity between them. We analyze the ICWSM’11 Spinn3r dataset containing over 60 million English documents. We observe surprising connections among the 161 events it covers, and that over half (55%) of users only link to a small fraction of prolific users (4%), a notable departure from the balanced traditional bow-tie model of web content.
What Were the Tweets About? Topical Associations between Public Events and Twitter Feeds
Authors: Hu Yuheng , John Ajita , Seligmann Dorée , Wang Fei
Published: November 2022

Social media channels such as Twitter have emerged as platforms for crowds to respond to public and televised events such as speeches and debates. However, the very large volume of responses presents challenges for attempts to extract sense from them. In this work, we present an analytical method based on joint statistical modeling of topical influences from the events and associated Twitter feeds. The model enables the auto-segmentation of the events and the characterization of tweets into two categories: (1) episodic tweets that respond specifically to the content in the segmentsof the events, and (2) steady tweets that respond generally about the events. By applying our method to two large sets of tweets in response to President Obama's speech on the Middle East in May 2011 and a Republican Primary debate in September 2011, we present what these tweets were about. We also reveal the nature and magnitude of the influences of the event on the tweets over its timeline. In a user study, we further show that users find the topics and the episodic tweets discovered by our method to be of higher quality and more interesting as compared to the state-of-the-art, with improvements in the range of 18-41%.
The Emergence of Conventions in Online Social Networks
Authors: Kooti Farshad , Yang Haeryun , Cha Meeyoung , Gummadi Krishna , Mason Winter
Published: November 2022

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.
Privacy in Interaction: Exploring Disclosure and Social Capital in Facebook
Authors: Stutzman Frederic , Vitak Jessica , Ellison Nicole , Gray Rebecca , Lampe Cliff
Published: November 2022

In this paper, we explore the relationship between Facebook users’ privacy concerns, relationship maintenance strategies, and social capital outcomes. Previous research has found a positive relationship between various measures of Facebook use and perceptions of social capital, i.e., one’s access to social and information-based resources. Other research has found that social network site users with high privacy concerns modify their disclosures on the site. However, no research to date has empirically tested how privacy concerns and disclosure strategies interact to influence social capital outcomes. To address this gap in the literature, we explored these questions with survey data (N=230). Findings indicate that privacy concerns and behaviors predict disclosures on Facebook, but not perceptions of social capital. In addition, when looking at predictors of social capital, we identify interaction effects between users’ network composition and their use of privacy features.
Facebook, Youth and Privacy in Networked Publics
Authors: Tufekci Zeynep
Published: November 2022

Media accounts would have us believe that today’s youth are a particularly narcissistic generation. Young adults are often portrayed as exhibitionists who share personal information excessively and only react if “burned” by experience. This paper reports results from 450 surveys of young adults on social network site usage and privacy and surveillance experiences--as well as from a historical archive dating back to 2006. The findings show a complex picture of a generation actively engaging visibility and social boundaries online through privacy and visibility practices. A striking increase in privacy protective activities is documented. I examine whether these changes are in response to personal negative experiences from online disclosure or if they derive from general awareness. I find that students are reacting pro-actively and adjusting their privacy settings above and beyond the impact of negative personal experiences. Contrary to media reports, young adults do not appear uncaring about privacy and are not waiting until they get burned. Significant racial and gender differences remain in privacy behaviors. Strikingly, about 20% report having deactivated their profile at least once.
Distributional Footprints of Deceptive Product Reviews
Authors: Feng Song , Xing Longfei , Gogar Anupam , Choi Yejin
Published: November 2022

This paper postulates that there are natural distributions of opinions in product reviews. In particular, we hypothesize that for a given domain, there is a set of representative distributions of review rating scores. A deceptive business entity that hires people to write fake reviews will necessarily distort its distribution of review scores, leaving distributional footprints behind. In order to validate this hypothesis, we introduce strategies to create dataset with pseudo-gold standard that is labeled automatically based on different types of distributional footprints. A range of experiments confirm the hypothesized connection between the distributional anomaly and deceptive reviews. This study also provides novel quantitative insights into the characteristics of natural distributions of opinions in the TripAdvisor hotel review and the Amazon product review domains.
Hiding in Plain Sight: A Tale of Trust and Mistrust inside a Community of Citizen Reporters
Authors: Mustafaraj Eni , Metaxas Panagiotis , Finn Samantha , Monroy-Hernandez Andres
Published: November 2022

The large volume of user-generated content on the Social Web puts a high burden on the participants to evaluate the accuracy and quality of such content. Researchers have been trying to address this problem by focusing on discovering in- fluential entities inside social platforms to guide their ranking algorithms. Often these entities correspond to people or orga- nizations already influential in the real-world. However, not everywhere in the world is there a right to and protection of free speech. In countries where the traditional media cannot report the truth, anonymity becomes a necessity for citizens who want to exercise their right of free-speech in the service of their community. But is it possible for anonymous individuals to become influ- ential and gain the trust of a community? In this paper, we discuss the case of a community of Twitter citizen reporters, located in a Mexican city plagued by the drug cartels fighting for control of territory. Our analysis shows that the most in- fluential individuals inside the community were anonymous accounts. Neither the Mexican authorities, nor the drug car- tels were happy about the real-time citizen reporting of crime or anti-crime operations in an open social network such as Twitter, and we discovered external pressures to this com- munity and its influential players to change their reporting behavior.
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City
Authors: Cranshaw Justin , Schwartz Raz , Hong Jason , Sadeh Norman
Published: November 2022

Studying the social dynamics of a city on a large scale has tra- ditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a par- tial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these ar- eas. To address both difficulties, we introduce a clustering model and research methodology for studying the structure and composition of a city based on the social media its res- idents generate. We use data from approximately 18 million check-ins collected from users of a location-based online so- cial network. The resulting clusters, which we call Livehoods, are representations of the dynamic urban areas that comprise the city. We take an interdisciplinary approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly charac- terized areas of the city and the forces that shape them.
Network Analysis of Recurring YouTube Spam Campaigns
Authors: O'Callaghan Derek , Harrigan Martin , Carthy Joe , Cunningham Pádraig
Published: November 2022

As the popularity of content sharing websites has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to malicious third-party websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies, and present an evaluation that tracks two corresponding active campaigns.
Epidemic Intelligence for the Crowd, by the Crowd
Authors: Diaz-Aviles Ernesto , Stewart Avaré , Velasco Edward , Denecke Kerstin , Nejdl Wolfgang
Published: November 2022

Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.
The Good, the Bad, and the Angry: Analyzing Crowdsourced Impressions of Vloggers
Authors: Biel Joan-Isaac , Gatica-Perez Daniel
Published: November 2022

We address the study of interpersonal perception in social conversational video based on multifaceted impressions collected from short video-watching. First, we crowdsourced the annotation of personality, attractiveness, and mood impressions for a dataset of YouTube vloggers, generating a corpora that has potential to develop automatic techniques for vlogger characterization. Then, we provide an analysis of the crowdsourced annotations focusing on the level of agreement among annotators, as well as the interplay between different impressions. Overall, this work provides interesting new insights on vlogger impressions and the use of crowdsourcing to collect behavioral annotations from multimodal data.
Tag Recommendation by Link Prediction Based on Supervised Machine Learning
Authors: Pujari Manisha , Kanawati Rushed
Published: November 2022

In this work, we explore applying a link prediction approach to tag recommendation in broad folksonomies. The original idea of the approach is to mine the dynamic of the tagging activity in order to compute the most suitable tag for a given user and a given resource. The tagging history of each user is modeled by a temporal sequence of bipartite graphs linking tags to resources. Given a target user and a target resource, we first compute a set of similar users. The tagging history of the identified set of users is merged in one temporal sequence on bipartite graphs. The obtained sequence is used to learn a model of link prediction in bipartite graphs. The learned model is then applied to predict tags to be linked to the target resource and a list of top similar resources. We get hence several ranked lists tags, one list for each considered resource. These ranked lists are then merged, applying classical preference merging methods in order to obtain the final output: a list of ranked tags that will be recommended to the user. We show through experiments conducted on real datasets extracted for the CiteULike folksonomy the soundness of the proposed approach.
Visualizing Topic Models
Authors: Chaney Allison , Blei David
Published: November 2022

Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method.
Social Network Sites: Indispensable or Optional Social Tools?
Authors: Shklovski Irina
Published: November 2022

Much research has enumerated potential benefits of online social network sites. Given the pervasiveness of these sites and the numbers of people that use them daily, both research and media tend to make the assumption that social network sites have become indispensible to their users. Based on the analysis of qualitative data from users of social network sites in Russia and Kazakhstan, this paper considers under what conditions social network sites can become indispensable to their users and when these technologies remain on the periphery of life despite fulfilling useful functions. For some respondents, these sites had become indispensable tools as they were integrated into everyday routines of communicating with emotionally important and proximal contacts and were often used for coordination of offline activities. For others social network sites remained spaces where they occasionally visited with people who may have been important at some point in the past but who had little connection to the daily business of living. In these cases social network sites were seen as convenient spaces of lightweight connectivity, but by no means indispensable.
An Evaluation of the Role of Sentiment in Second Screen Microblog Search Tasks
Authors: Bermingham Adam , Smeaton Alan
Published: November 2022

The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user's query at a point in time, automated methods are required to sift through this information. Sentiment analysis offers a promising direction for modelling microblog content. We build and evaluate a sentiment-based filtering system using real-time user studies. We find a significant role played by sentiment in the search scenarios, observing detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users' prior topic sentiment.
Happy, Nervous or Surprised? Classification of Human Affective States in Social Media
Authors: De Choudhury Munmun , Gamon Michael , Counts Scott
Published: November 2022

Sentiment classification has been a well-investigated research area in the computational linguistics community. However, most of the research is primarily focused on detecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little attention has been directed towards a finer analysis of human moods and affective states. Motivated by research in psychology, we propose and develop a classifier of several human affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive naturalistic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an affect classification task with promising results. Our findings indicate that different types of affect involve different emotional content and usage styles; hence the performance of the classifier on various affects can differ considerably.
Workshop Mission
Authors: Spangenberg Jochen
Published: November 2022

The aim of this workshop is to encourage participants to discuss, share, and exchange ideas and results on social media research, technologies, and applications in two essential areas: newsgathering in the social web and filtering or analyzing the available data (for exam- ple, by relevance, reliability, or accuracy).
Visualizing a Personal Timeline By Adding Multiple Social Contexts
Authors: Kwak Haewoon , Hong Yoonsung , You Jinyoung , Moon Sue
Published: November 2022

We improve the readability of a personal timeline byweaving multiple social contexts of tweets into a visualization.Our social contexts consist of three dimensions:community membership, key persons, and interestingtweets within a personal timeline. A person is oftena member of several communities, such as a family,a class, or a team, simultaneously. We identify all communitiesthat a user participates in. Labeling a tweetwith a visual representation to indicate what communityit belongs to can help readers to understand why thetweet is written, since different communities are likelyto carry tweets in different contexts. We then discoverkey persons and interesting tweets within a personaltimeline. Our prototype design demonstrates how threesocial contexts work together for visualizing a personaltimeline.
Workshops
Authors: Kiciman Emre
Published: November 2022

The workshops presented at ICWSM 2013 included Computational Personality Recognition (Shared Task), Social Computing for Workforce 2.0, Social Media Visualization, and When The City Meets The Citizen.
A Crowd-Powered Socially Embedded Search Engine
Authors: Jeong Jin-Woo , Morris Meredith , Teevan Jaime , Liebling Dan
Published: November 2022

People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the replies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced” answers with crowdsourced answers. By analyzing of thousands of public Twitter questions and answers, we build a picture of which questions receive answers and the content of their answers. Because many questions seek subjective responses but go unanswered, we use crowdsourcing to augment the Twitter question asking experience. We deploy a system that uses the crowd to identify question tweets, create candidate replies, and vote on the best reply from among different crowd- and friend-generated answers. We find that crowdsourced answers are similar in nature and quality to friendsourced answers, and that almost a third of all question askers provided unsolicited positive feedback upon receiving answers from this novel information agent.
What Yelp Fake Review Filter Might Be Doing?
Authors: Mukherjee Arjun , Venkataraman Vivek , Liu Bing , Glance Natalie
Published: November 2022

Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.
Agenda Diversity in Social Media Discourse: A Study of the 2012 Korean General Election
Authors: Park Souneil , Ko Minsam , Lee Jaeung , Song Junehwa
Published: November 2022

The diversity of agendas in the media plays a critical role in shaping the scope of public discourse. Due to the increasing impact of social media, it is important and necessary to examine if it is circulating a wide range of issue and extending the realm of public discussion. In this paper, we put forward agenda diversity as a core value for evaluation of social media discourse and examine the current diversity status. Our study provides a firm basis for understanding the topics, quantity, diversity, and relative salience of the agendas offered by the social media. We further look into the information diffusion process from the diversity viewpoint, and explore the potential to increase diversity.
Self-Censorship on Facebook
Authors: Das Sauvik , Kramer Adam
Published: November 2022

We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.
A Measure of Polarization on Social Media Networks Based on Community Boundaries
Authors: Guerra Pedro , Meira Jr. Wagner , Cardie Claire , Kleinberg Robert
Published: November 2022

Polarization in social media networks is a fact in several scenarios such as political debates and other contexts such as same-sex marriage, abortion and gun control. Understanding and quantifying polarization is a long-term challenge to researchers from several areas, also being a key information for tasks such as opinion analysis. In this paper, we perform a systematic comparison between social networks that arise from both polarized and non-polarized contexts. This comparison shows that the traditional polarization metric -modularity - is not a direct measure of antagonism between groups, since non-polarized networks may be also divided into fairly modular communities. To bridge this conceptual gap, we propose a novel polarization metric based on the analysis of the boundary of a pair of (potentially polarized) communities, which better captures the notions of antagonism and polarization. We then characterize polarized and non-polarized social networks according to the concentration of high-degree nodes in the boundary of communities, and found that polarized networks tend to exhibit low concentration of popular nodes along the boundary. To demonstrate the usefulness of our polarization measures, we analyze opinions expressed on Twitter on the gun control issue in the United States, and conclude that our novel metrics help making sense of opinions expressed on online media.
Quantifying Political Leaning from Tweets and Retweets
Authors: Wong Felix Ming Fai , Tan Chee Wei , Sen Soumya , Chiang Mung
Published: November 2022

Media outlets and pundits have been quick to embrace online social networks to disseminate their own opinions. But pundits’ opinions and news coverage are often marked by a clear political bias, as widely evidenced during the fiercely contested2012 U.S. presidential elections. Given the wide availability of such data from sites like Twitter, a natural question is whether we can quantify the political leanings of media outlets using OSN data. In this work, by drawing a correspondence between tweeting and retweeting behavior, we formulate political leaning estimation as an ill-posed linear inverse problem. The result is a simple and scalable approach that does not require explicit knowledge of the network topology. We evaluate our method with a dataset of 119 million election-related tweets collected from April to November, and use it to study the political leaning of prominent tweeters and media sources.
Ladies First: Analyzing Gender Roles and Behaviors in Pinterest
Authors: Ottoni Raphael , Pesce João Paulo , Las Casas Diego , Franciscani Jr. Geraldo , Meira Jr. Wagner , Kumaraguru Ponnurangam , Almeida Virgilio
Published: November 2022

Online social networks (OSNs) have become popular platforms for people to connect and interact with each other. Among those networks, Pinterest has recently become noteworthy for its growth and promotion of visual over textual content. The purpose of this study is to analyze this image-based network in a gender-sensitive fashion, in order to un- derstand (i) user motivation and usage pattern in the network, (ii) how communications and social interactions happen and (iii) how users describe themselves to others. This work is based on more than 220 million items generated by 683,273 users. We were able to find significant differences w.r.t. all mentioned aspects. We observed that, although the network does not encourage direct social communication, females make more use of lightweight interactions than males. Moreover, females invest more effort in reciprocating social links, are more active and generalist in content generation, and describe themselves using words of affection and positive emotions. Males, on the other hand, are more likely to be specialists and tend to describe themselves in an assertive way. We also observed that each gender has different interests in the network, females tend to make more use of the network's commercial capabilities, while males are more prone to the role of curators of items that reflect their personal taste. It is important to understand gender differences in online social networks, so one can design services and applications that leverage human social interactions and provide more targeted and relevant user experiences.
Local Knowledge Matters for Crowdsourcing Systems: Experience from Transferring an American Site to China
Authors: Torre Fernando , Liu Yanjie , Liu Zhengjie , Terveen Loren
Published: November 2022

We present the results of a preliminary cross-cultural study of Cyclopath, a geographic crowdsourcing system for cyclists. We created a Chinese version and tested it in lab with 40 cyclists in Dalian, China. We analyzed the data entered by users and found some significant differences compared to similar studies done in the US; this illustrates the need for local knowledge in crowdsourcing applications. From usage patterns and interview data, we observed that landmarks play a larger role for navigating the map and requesting routes; this suggests that integrating landmarks better into the Cyclopath UI could improve Chinese users’ experience with the system. Finally, we found that concepts of authority and hierarchy may influence Chinese users’ trust of peer-contributed content; this suggests that reputation mechanisms may help improve trust.
Sentiment Prediction Using Collaborative Filtering
Authors: Kim Jihie , Yoo Jaebong , Lim Ho , Qiu Huida , Kozareva Zornitsa , Galstyan Aram
Published: November 2022

Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.
Organizers
Authors: Archambault Daniel
Published: November 2022

List of organizers of the AAAI ICWSM Workshop on Social Media Visualization 2.
Scalable Social Analytics for Live Viral Event Prediction
Authors: Jain Puneet , Manweiler Justin , Acharya Arup , Roy Choudhury Romit
Published: November 2022

Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception. This paper proposes CrowdCast, a cloud-based framework to enable real-time analysis and prediction from streaming social data. As an instantiation of this framework, we tune CrowdCast to observe Twitter tweets, and predict which YouTube videos are most likely to “go viral” in the near future. To this end, CrowdCast first applies online machine learning to map natural language tweets to a specific YouTube video. Then, tweets that indeed refer to videos are weighted by the perceived “influence” of the sender. Finally, the video’s spread is predicted through a sociological model, derived from the emerging structure of the graph over which the video-related tweets are (still) spreading. Combining metrics of influence and live structure, CrowdCast outputs sets of candidate videos, identified as likely to become viral in the next few hours. We monitor Twitter for more than 30 days, and find that CrowdCast’s real-time predictions demonstrate encouraging correlation with actual YouTube viewership in the near future.
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls
Authors: Tufekci Zeynep
Published: November 2022

Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with special attention to the validity and representativeness of social media big data analyses. Persistent issues include the over-emphasis of a single platform, Twitter, sampling biases arising from selection by hashtags, and vague and unrepresentative sampling frames. The socio-cultural complexity of user behavior aimed at algorithmic invisibility (such as subtweeting, mock-retweeting, use of “screen captures” for text, etc.) further complicate interpretation of big data social media. Other challenges include accounting for field effects, i.e. broadly consequential events that do not diffuse only through the network under study but affect the whole society. The application of network methods from other fields to the study of human social activity may not always be appropriate. The paper concludes with a call to action on practical steps to improve our analytic capacity in this promising, rapidly-growing field.
Quantifying Political Polarity Based on Bipartite Opinion Networks
Authors: Akoglu Leman
Published: November 2022

Political inclinations of individuals (liberal vs. conservative) largely shape their opinions on several issues such as abortion, gun control, nuclear power, etc. These opinions are openly exerted inonline forums, news sites, the parliament, and so on. In this paper, we address the problem of quantifying political polarity of individuals and of political issues for classification and ranking. We use signed bipartite networks to represent the opinions of individuals on issues, and formulate the problem as a node classification task. We propose a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner. Through extensive experiments we demonstrate that our proposed method provides an effective, fast, and easy-to-implement solution, while outperforming three existing baseline algorithms adapted to signed networks, on real political forum and US Congress datasets.Experiments on a wide variety of synthetic graphs with varying polarity and degree distributions of the nodes further demonstrate the robustness of our approach.
The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing
Authors: Lee Kyumin , Webb Steve , Ge Hancheng
Published: November 2022

As human computation on crowdsourcing systems has become popular and powerful for performing tasks, malicious users have started misusing these systems by posting malicious tasks, propagating manipulated contents, and targeting popular web services such as online social networks and search engines. Recently, these malicious users moved to Fiverr, a fast-growing micro-task marketplace, where workers can post crowdturfing tasks (i.e., astroturfing campaigns run by crowd workers) and malicious customers can purchase those tasks for only $5. In this paper, we present a comprehensive analysis of Fiverr. First, we identify the most popular types of crowdturfing tasks found in this marketplace and conduct case studies for these crowdturfing tasks. Then, we build crowdturfing task detection classifiers to filter these tasks and prevent them from becoming active in the marketplace. Our experimental results show that the proposed classification approach effectively detects crowdturfing tasks, achieving 97.35% accuracy. Finally, we analyze the real world impact of crowdturfing tasks by purchasing active Fiverr tasks and quantifying their impact on a target site. As part of this analysis, we show that current security systems inadequately detect crowdsourced manipulation, which confirms the necessity of our proposed crowdturfing task detection approach.
Predicting Successful Memes Using Network and Community Structure
Authors: Weng Lilian , Menczer Filippo , Ahn Yong-Yeol
Published: November 2022

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Event Detection via Communication Pattern Analysis
Authors: Chierichetti Flavio , Kleinberg Jon , Kumar Ravi , Mahdian Mohammad , Pandey Sandeep
Published: November 2022

Social media applications such as Twitter provide a powerful medium through which users can communicate their observations with friends and with the world at large. We have witnessed live reporting of many events, from soccer games in Johannesburg to revolutions in Cairo and Tunis, and these reports have in many ways rivaled the content provided by the official media. Tapping into this valuable resource is a challenge, due to the heterogeneity and noise inherent in realtime text, diversity of languages, and fast-evolving linguistic norms. In this paper we seek to analyze a tweet stream to automatically discover points in time when an important event happens, and to classify such events based on the type of the sentiments they evoke, using only non-textual features of the tweeting pattern. This results not only in a robust way of analyzing tweet streams independent of the languages used; it also provides insights about how users behave on social media websites. For example, we observe that users often react to an exciting external event by decreasing the volume of communication with other users. We explain this effect through a model of how users switch between producing information or sentiments and sharing others’ news or sentiments. We develop and evaluate our models and algorithms using several Twitter data sets, focusing in particular on the tweets sent during the soccer World Cup of 2010. This data set has the feature that the underlying ground truth is welldefined and known whereby goals serve as events.