bluuebunny/crossref_metadata_2025 huggingface dataset:
Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events
Authors: Lambert Charlotte , Rajagopal Ananya , Chandrasekharan Eshwar
Published: November 2022

Online conversations, just like offline ones, are susceptible to influence by bad actors. These users have the capacity to derail neutral or even prosocial discussions through adverse behavior. Moderators and users alike would benefit from more resilient online conversations, i.e., those that can survive the influx of adverse behavior to which many conversations fall victim. In this paper, we examine the notion of conversational resilience: what makes a conversation more or less capable of withstanding an adverse interruption? Working with 11.5M comments from eight mainstream subreddits, we compiled more than 5.8M comment threads (i.e., conversations). Using 239K relevant conversations, we examine how well comment, user, and subreddit characteristics can predict conversational outcomes. More than half of all conversations proceed after the first adverse event. Six out of ten conversations that proceed result in future removals. Comments violating platform-wide norms and those written by authors with a history of norm violations lead to not only more norm violations, but also fewer prosocial outcomes. However, conversations in more populated subreddits and conversations where the first adverse event's author was initially a strong contributor are capable of minimizing future removals and promoting prosocial outcomes after an adverse event. By understanding factors that contribute to conversational resilience we shed light onto what types of behavior can be encouraged to promote prosocial outcomes even in the face of adversity.
Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic
Authors: Le Quéré Marianne Aubin , Chiang Ting-Wei , Naaman Mor
Published: November 2022

During the COVID-19 pandemic, local news organizations have played an important role in keeping communities informed about the spread and impact of the virus. We explore how political, social media, and economic factors impacted the way local media reported on COVID-19 developments at a national scale between January 2020 and July 2021. We construct and make available a dataset of over 10,000 local news organizations and their social media handles across the U.S. We use social media data to estimate the population reach of outlets (their “localness”), and capture underlying content relationships between them. Building on this data, we analyze how local and national media covered four key COVID-19 news topics: Statistics and Case Counts, Vaccines and Testing, Public Health Guidelines, and Economic Effects. Our results show that news outlets with higher population reach reported proportionally more on COVID-19 than more local outlets. Separating the analysis by topic, we expose more nuanced trends, for example that outlets with a smaller population reach covered the Statistics and Case Counts topic proportionally more, and the Economic Effects topic proportionally less. Our analysis further shows that people engaged proportionally more and used stronger reactions when COVID-19 news were posted by outlets with a smaller population reach. Finally, we demonstrate that COVID-19 posts in Republican-leaning counties generally received more comments and fewer likes than in Democratic counties, perhaps indicating controversy.
Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics
Authors: Lee JooYoung , Wu Siqi , Ertugrul Ali Mert , Lin Yu-Ru , Xie Lexing
Published: November 2022

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms.
Glowing Experience or Bad Trip? A Quantitative Analysis of User Reported Drug Experiences on Erowid.org
Authors: Mooseder Angelina , Malik Momin M. , Lamba Hemank , Erowid Earth , Thyssen Sylvia , Pfeffer Jürgen
Published: November 2022

Erowid.org is a website dedicated to documenting information about psychoactive substances, with over 36,000 user-submitted drug Experience Reports. We study the potential of these reports to provide information about characteristic experiences with drugs. First, we assess different kinds of drug experiences, such as 'addiction' or 'bad trips'. We quantitatively analyze how such experiences are related to substances and user variables. Furthermore, we classify positive and negative experiences as well as reported addiction using information about the consumer, substance, context and location of the drug experience. While variables based only on objective characteristics yield poor predictive performance for subjective experiences, we find subjective user reports can help to identify new patterns and impact factors on drug experiences. In particular, we found a positive association between addiction experiences and dextromethorphan, a substance with largely unknown withdrawal effects. Our research can help to gain a deeper sociological understanding of drug consumption and to identify relationships which may have clinical relevance. Moreover, it can show how non-mainstream social media platforms can be utilized to study characteristics of human behavior and how this can be done in an ethical way in collaboration with the platform providers.
Mapping Topics in 100,000 Real-Life Moral Dilemmas
Authors: Nguyen Tuan Dung , Lyall Georgiana , Tran Alasdair , Shin Minjeong , Carroll Nicholas George , Klein Colin , Xie Lexing
Published: November 2022

Moral dilemmas play an important role in theorizing both about ethical norms and moral psychology. Yet thought experiments borrowed from the philosophical literature often lack the nuances and complexity of real life. We leverage 100,000 threads—the largest collection to date—from Reddit’s r/AmItheAsshole to examine the features of everyday moral dilemmas. Combining topic modeling with evaluation from both expert and crowd-sourced workers, we discover 47 fine-grained, meaningful topics and group them into five meta-categories. We show that most dilemmas combine at least two topics, such as family and money. We also observe that the pattern of topic co-occurrence carries interesting information about the structure of everyday moral concerns: for example, the generation of moral dilemmas from nominally neutral topics, and interaction effects in which final verdicts do not line up with the moral concerns in the original stories in any simple way. Our analysis demonstrates the utility of a fine-grained data-driven approach to online moral dilemmas, and provides a valuable resource for researchers aiming to explore the intersection of practical and theoretical ethics.
Manipulating Twitter through Deletions
Authors: Torres-Lugo Christopher , Pote Manita , Nwala Alexander C. , Menczer Filippo
Published: November 2022

Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. By design, these approaches ignore deleted tweets. However, bad actors can delete content strategically to manipulate the system. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. Estimates based on publicly available Twitter data underestimate the true deletion volume. A small fraction of accounts delete a large number of tweets daily. We uncover two abusive behaviors that exploit deletions. First, limits on tweet volume are circumvented, allowing certain accounts to flood the network with over 26 thousand daily tweets. Second, coordinated networks of accounts engage in repetitive likes and unlikes of content that is eventually deleted, which can manipulate ranking algorithms. These kinds of abuse can be exploited to amplify content and inflate popularity, while evading detection. Our study provides platforms and researchers with new methods for identifying social media abuse.
Modeling Latent Dimensions of Human Beliefs
Authors: Vu Huy , Giorgi Salvatore , Clifton Jeremy D. W. , Balasubramanian Niranjan , Schwartz H. Andrew
Published: November 2022

How we perceive our surrounding world impacts how we live in and react to it. In this study, we propose LaBel (Latent Beliefs Model), an alternative to topic modeling that uncovers latent semantic dimensions from transformer-based embeddings and enables their representation as generated phrases rather than word lists. We use LaBel to explore the major beliefs that humans have about the world and other prevalent domains, such as education or parenting. Although human beliefs have been explored in previous works, our proposed model helps automate the exploring process to rely less on human experts, saving time and manual efforts, especially when working with large corpus data. Our approach to LaBel uses a novel modification of autoregressive transformers to effectively generate texts conditioning on a vector input format. Differently from topic modeling methods, our generated texts (e.g. “the world is truly in your favor”) are discourse segments rather than word lists, which helps convey semantics in a more natural manner with full context. We evaluate LaBel dimensions using both an intrusion task as well as a classification task of identifying categories of major beliefs in tweets finding greater accuracies than popular topic modeling approaches.
Judging a Book by Its Cover: Predicting the Marginal Impact of Title on Reddit Post Popularity
Authors: Weissburg Evan , Kumar Arya , Dhillon Paramveer S.
Published: November 2022

Several factors influence the popularity of content on social media, including the what, when, and who of a post. Of these factors, the what and when of content are easiest to customize in order to maximize viewership and reach. Further, the title of a post (part of the what) is the easiest to tailor, compared to the post's body, which is often fixed. So, in this paper, we assess the impact of a post's title on its popularity while controlling for the time of posting (the when) by proposing an interpretable attention-based model. Our approach achieves state-of-the-art performance on predicting the popularity of posts on multiple online communities of various sizes, topics, and formats while still being parsimonious. Interpretation of our model's attention weights sheds light on the heterogeneous patterns in how the specific words in a post's title shape its popularity across different communities. Our results highlight the power of sentiment alignment, personal storytelling, and even personality politics in propelling content to virality.
Twitter Corpus of the #BlackLivesMatter Movement and Counter Protests: 2013 to 2021
Authors: Giorgi Salvatore , Guntuku Sharath Chandra , Himelein-Wachowiak Mckenzie , Kwarteng Amy , Hwang Sy , Rahman Muhammad , Curtis Brenda
Published: November 2022

Black Lives Matter (BLM) is a decentralized social movement protesting violence against Black individuals and communities, with a focus on police brutality.
The movement gained significant attention following the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd in 2020. The #BlackLivesMatter social media hashtag has come to represent the grassroots movement, with similar hashtags counter protesting the BLM movement, such as #AllLivesMatter, and #BlueLivesMatter. We introduce a data set of 63.9 million tweets from 13.0 million users from over 100 countries which contain one of the following keywords: BlackLivesMatter, AllLivesMatter, and BlueLivesMatter. This data set contains all currently available tweets from the beginning of the BLM movement in 2013 to 2021. We summarize the data set and show temporal trends in use of both the BlackLivesMatter keyword and keywords associated with counter movements.
Additionally, for each keyword, we create and release a set of Latent Dirichlet Allocation (LDA) topics (i.e., automatically clustered groups of semantically co-occuring words) to aid researchers in identifying linguistic patterns across the three keywords.
A Large-Scale Longitudinal Multimodal Dataset of State-Backed Information Operations on Twitter
Authors: Guo Xiaobo , Vosoughi Soroush
Published: November 2022

This paper proposes a large-scale and comprehensive dataset of 28 sub-datasets of state-backed tweets and accounts affiliated with 14 different countries, spanning more than 3 years, and a corresponding "negative" dataset of background tweets from the same time period and on similar topics. To our knowledge, this is the first dataset that contains both state-sponsored propaganda tweets and carefully collected corresponding negative tweet datasets for so many countries spanning such a long period of time.
The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse
Authors: Hofmann Valentin , Schütze Hinrich , Pierrehumbert Janet B.
Published: November 2022

We introduce the Reddit Politosphere, a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. It is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data, allowing for methodologically-diverse analyses. We describe in detail how we create the Reddit Politosphere, present descriptive statistics, and sketch potential directions for future research based on the resource.
NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-Level News Ecosystems
Authors: Horne Benjamin D. , Gruppi Maurício , Joseph Kenneth , Green Jon , Wihbey John P. , Adalı Sibel
Published: November 2022

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K.
MMCHIVED: Multimodal Chile and Venezuela Protest Event Data
Authors: Steinert-Threlkeld Zachary , Joo Jungseock
Published: November 2022

This paper introduces the Multimodal Chile & Venezuela Protest Event Dataset (MMCHIVED). MMCHIVED contains city-day event data using a new source of data, text and images shared on social media. These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions. In Venezuela, MMCHIVED records many more protests than existing datasets. In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED). These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14% of global GDP and 82.7% of the world's population.
The MeLa BitChute Dataset
Authors: Trujillo Milo Z. , Gruppi Maurício , Buntain Cody , Horne Benjamin D.
Published: November 2022

In this paper we present a near-complete dataset of over 3M videos from 61K channels over 2.5 years (June 2019 to December 2021) from the social video hosting platform BitChute, a commonly used alternative to YouTube. Additionally, we include a variety of video-level metadata, including comments, channel descriptions, and views for each video. The MeLa-BitChute dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS.
Network Analysis of the Information Consumption-Production Dichotomy in Mastodon User Behaviors
Authors: La Cava Lucio , Greco Sergio , Tagarelli Andrea
Published: November 2022

Decentralized Online Social Networks (DOSNs) are today an established alternative to the popular centralized counterparts. In this work, we push forward research on user behaviors in a decentralized context, by exploring the dichotomy between information consumption and production. Using the Mastodon user network as a proxy for the Fediverse landscape, we address two main research questions: Do the consumers, resp. producers, identified in one instance exhibit the same behavior consistently while interacting with other instances? and, Are there users who behave as consumers in one instance and simultaneously as producers in other instances, or vice versa? In this respect, our results reveal interesting traits of Mastodon users, yet unveil the emergence for further studies that can embrace other services in the Fediverse.
The Contribution of Verified Accounts to Self-Disclosure in COVID-Related Twitter Conversations
Authors: Du Tingting , Umar Prasanna , Rajtmajer Sarah , Squicciarini Anna
Published: November 2022

On Twitter, so-called verified accounts represent celebrities and organizations of public interest, selected by Twitter based on criteria for both activity and notability. Our work seeks to understand the involvement and influence of these accounts in patterns of self-disclosure, namely, voluntary sharing of personal information. In a study of 3 million COVID-19 related tweets, we present a comparison of self-disclosure in verified vs ordinary users. We discuss evidence of peer effects on self-disclosing behaviors and analyze topics of conversation associated with these practices.
Measuring Media Bias via Masked Language Modeling
Authors: Guo Xiaobo , Ma Weicheng , Vosoughi Soroush
Published: November 2022

Bias in news reporting can lead to tribalism and division on important issues. Scalable and reliable measurement of such biases is an important first step in addressing them. In this work, based on the intuition that media bias is captured by the tone and word choices in articles, we propose a framework for modeling the relative bias of media outlets through masked token prediction via large-scale pretrained masked language models fine-tuned on articles form news outlets. Through experiments on five diverse and politically polarized topics we show that our framework can capture media bias towards these topics with high reliability. Additionally, our experiments show that our framework is general, in that language models fine-tuned on one topic can be applied to other topics with little drop in performance.
BLEWS: Using Blogs to Provide Context for News Articles
Authors: Gamon Michael , Basu Sumit , Belenko Dmitriy , Fisher Danyel , Hurst Matthew
Published: November 2022

An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article's social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc.
Mining and Visualizing Online Web Content Using BAM: Brand Association Map
Authors: Akiva Navot , Greitzer Eliyahu , Krichman Yakir , Schler Jonathan
Published: November 2022

In this paper, we describe our Brand Association Map (BAM) tool which maps and visualizes the way consumers naturally think and talk about brands across billions of unaided conversations online. BAM is a semi-supervised tool that leverages text-mining algorithms to identify key correlated phrases, terms and issues out of millions of candidate terms which were derived from billions of online conversations. The most correlated phrases with a given brand are then projected and plotted onto visual bull's eye representation. BAM's visualization illustrates both the correlation level between a brand (appears in the center of the visualization) and each of the highly correlated terms as well as the inner correlations among all presented terms, where terms on the same radial angel represent a "clustered" discussion of terms frequently mentioned together. We found BAM useful for extracting various intuitions and beliefs that are highly correlated with brands to better grasp how consumers really contextualize them, out of massive consumer generated media (CGM) documents.
Collecting and Analyzing Japanese Splogs based on Characteristics of Keywords
Authors: Sato Yuuki , Utsuro Takehito , Fukuhara Tomohiro , Kawada Yasuhide , Murakami Yoshiaki , Nakagawa Hiroshi , Kando Noriko
Published: November 2022

This paper focuses on analyzing (Japanese) splogs based on various characteristics of keywords contained in them. We estimate the behavior of spammers when creating splogs from other sources by analyzing the characteristics of keywords contained in splogs. Since splogs often cause noises in word occurrence statistics in the blogosphere, we assume that we can efficiently collect splogs by sampling blog homepages containing keywords of a certain type on the date with its most frequent occurrence. We manually examine various features of collected blog homepages regarding whether their text content is excerpt from other sources or not, as well as whether they display affiliate advertisement or out-going links to affiliated sites. Among various informative results, it is important to note that more than half of the collected splogs are created by a very small number of spammers.
Gesundheit! Modeling Contagion through Facebook News Feed
Authors: Sun Eric , Rosenn Itamar , Marlow Cameron , Lento Thomas
Published: November 2022

Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades. While reasonable under some conditions, this assumption may not hold for social media networks, where user engagement is high and information may enter a system from multiple disconnected sources. Using a dataset of 262,985 Facebook Pages and their associated fans, this paper provides an empirical investigation of diffusion through a large social media network. Although Facebook diffusion chains are often extremely long (chains of up to 82 levels have been observed), they are not usually the result of a single chain-reaction event. Rather, these diffusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together. This paper presents an analysis of these diffusion chains using zero-inflated negative binomial regressions. We show that after controlling for distribution effects, there is no meaningful evidence that a start node’s maximum diffusion chain length can be predicted with the user's demographics or Facebook usage characteristics (including the user's number of Facebook friends). This may provide insight into future research on public opinion formation.
Motivational, Structural and Tenure Factors that Impact Online Community Photo Sharing
Authors: Nov Oded , Naaman Mor , Ye Chen
Published: November 2022

In recent years, we have witnessed a significant growth of "social computing" services, or online communities where users contribute content in various forms, including images, text or video. Content contribution from members is critical to the viability of these online communities. It is therefore important to understand what drives users to share content with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with users' photo sharing in an online community, drawing on motivation theories as well as on analysis of basic structural properties. Our results indicate that photo sharing declines in respect to the users' tenure in the community. We also show that users with higher commitment to the community and greater "structural embeddedness" tend to share more content. We demonstrate that the motivation of self-development is negatively related to photo sharing, and that tenure in the community moderates the effect of self-development on photo sharing. Directions for future research, as well as implications for theory and practice are discussed.
Navigation Patterns from and to Social Media
Authors: Gamon Michael , König Arnd
Published: November 2022

With the rapid rise of social media content, the question how people navigate to and from social media becomes important in order to understand what new tools and approaches are most beneficial to allow users to discover and interact with this social media data. In this study we analyze session log data to describe navigation patterns involving these sites. Our findings suggest that there are distinct patterns of insular navigation behavior around social media, with blogs being the most transitory in the way users interact with them.
Optimal Heterophily for Word-of-Mouth Diffusion
Authors: Yamamoto Hikaru , Matsumura Naohiro
Published: November 2022

Most past research on word-of-mouth (WOM) communication has focused on the attributes of the WOM sender, such as opinion leadership and innovativeness. However, since WOM communication involves the interaction of the sender and the receiver, it is important to examine the relationship between the two. Past research on personal influence which examines the relational context includes two contradicting arguments: one supporting the power of homophily, and the other supporting the power of heterophily.　In this paper, the authors focus on the social relation between WOM senders and receivers and attempt to find the optimal heterophily for WOM diffusion.  The results of empirical analysis using both offline survey data and online blogosphere provide evidence of optimal heterophily between WOM sender and receiver. Finally, the patterns of influence are presented to depict the diffusion process.
Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection
Authors: Arnold Andrew , Cohen William
Published: November 2022

In this paper we explore the usefulness of various types of publication-related metadata, such as citation networks and curated databases, for the task of identifying genes in academic biomedical publications.  Specifically, we examine whether knowing something about which genes an author has previously written about, combined with information about previous coauthors and citations, can help us predict which new genes the author is likely to write about in the future.  Framed in this way, the problem becomes one of predicting links between authors and genes in the publication network.  We show that this solely social-network based link prediction technique outperforms various baselines, including those relying only on non-social biological information.
Is Your Profile Picture Worth 1000 Words? Photo Characteristics Associated with Personality Impression Agreement
Authors: Steele, Jr. Fitzgerald , Evans David , Green Rachel
Published: November 2022

Social-Networking Websites (SNWs) are rapidly becoming a central media for social exchange. A basic question is how well are people able to get to know each other through these websites? In this study, we explore characteristics of the profile photographs and their association with impression agreement. Using a specially designed social networking website, we examined 1,316 first-impressions of profile owners who had posted photographs as part of a complete profile. The results suggest that photographs in which the profile owners were smiling, outdoors, and shown with others were associated with higher impression agreement. Several gender interactions suggested that other aspects of the photographs, including head covering and apparent weight, also affected impression agreement depending on the gender of the profile owner and visitors. These results were interpreted in light of the literature on interpersonal perception.
Network Analysis of an Emergent Massively Collaborative Creation Community: How Can People Create Videos Collaboratively without Collaboration?
Authors: Hamasaki Masahiro , Takeda Hideaki , Hope Tom , Nishimura Takuichi
Published: November 2022

The Web technology enables numerous people to collaborate in creation. We designate it as massively collaborative creation via the Web. It is becoming an important activity such as Wikipedia and Yahoo! QA. As an example of massively collaborative creation, we particularly examine video development on Nico Nico Douga, which is a video sharing website that is popular in Japan. We specifically examine videos on Hatsune Miku, a version of a singing synthesizer application software that has inspired not only song creation but also songwriting, illustration, and video editing. As described herein, creators of interact to create new contents though their social network. We analyzed the process of developing thousands of videos based on creators' social networks. The social network reveals interesting features. Different categories of creators serve different roles in evolving the network. We also extracted communities from the network and observed different community structures and investigated the evolving nature of the network using motif analysis.
Analysis of Social Influence in Online Book Reviews
Authors: Sakunkoo Patty , Sakunkoo Nathan
Published: November 2022

It has been widely recognized that online opinions constitute important informational sources for consumers and producers. The open nature of communication supported by social media, however, raises an important yet unsettled question of whether and how earlier opinions affect those that come after. This poster presents a model to illustrate the relationship between existing and new reviews. Based on 12,500 Amazon reviews, our choice model shows support for the idea that social contagion may be an important mechanism guiding behaviors of online reviewers. The results thus offer novel insights toward a better understanding of contagious behaviors, as well as minority influence, among social media users.
From Episodes to Sagas: Understanding the News by Identifying Temporally Related Story Sequences
Authors: Balasubramanyan Ramnath , Lin Frank , Cohen William , Hurst Matthew , Smith Noah
Published: November 2022

News interfaces are largely driven by recent information, even if many events are better interpreted in context of previous events. To address this problem, we consider the task of constructing an explicit representation of a “saga”—a long-running series of related events. We define a timeline as a concrete representation of a “saga” and we propose two unsupervised methods for timeline construction and compare their performance to hand-produced timelines using a tree edit distance measure. Preliminary results using these techniques on a weblog corpus and a supplementary news corpus are presented, showing both promise and challenges.
ICWSM — A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews
Authors: Tsur Oren , Davidov Dmitry , Rappoport Ari
Published: November 2022

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semi-supervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.
Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks
Authors: Lerman Kristina , Ghosh Rumi
Published: November 2022

Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.
Measuring User Influence in Twitter: The Million Follower Fallacy
Authors: Cha Meeyoung , Haddadi Hamed , Benevenuto Fabricio , Gummadi Krishna
Published: November 2022

Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.
Temporal Correlation between Social Tags and Emerging Long-Term Trend Detection
Authors: Hsu Ming-Hung , Chang Yu-Hui , Chen Hsin-Hsi
Published: November 2022

Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends to tag recommendation.
Conversational Shadows: Describing Live Media Events Using Short Messages
Authors: Shamma David , Kennedy Lyndon , Churchill Elizabeth
Published: November 2022

Microblogging concurrently with live media events is becoming commonplace. The resulting comment stream represents a parallel, social conversational reflection on the event. Although not formally `attached' to the actual event stream itself, we demonstrate it is possible to establish a relationship between the two streams by mapping their structural properties. In this article, we examine: How do people produce and consume real-time commentary? And how does the structure of commentary and conversation change in response to moments of interest? Using a dataset of 53,712 Twitter posts, or tweets, sampled during the inauguration of Barack Obama in January 2009, we develop methods for exploring these questions. We find that short message activity reflects the structure and content of this media event. Specifically, messages directed at large audiences can serve as broadcast announcements, while variations in the level of conversation can reflect levels of interest in the media event itself. Finally, we present some implications for the design of future tools for a variety of users ranging from consumers to journalists.
Culture Matters: A Survey Study of Social Q&A Behavior
Authors: Yang Jiang , Morris Meredith , Teevan Jaime , Adamic Lada , Ackerman Mark
Published: November 2022

Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems.
When the Wikipedians Talk: Network and Tree Structure of Wikipedia Discussion Pages
Authors: Laniado David , Tasso Riccardo , Volkovich Yana , Kaltenbrunner Andreas
Published: November 2022

Talk pages play a fundamental role in Wikipedia as the place for discussion and communication. In this work we use the comments on these pages to extract and study three networks, corresponding to different kinds of interactions. We find evidence of a specific assortativity profile which differentiates article discussions from personal conversations. An analysis of the tree structure of the article talk pages allows to capture patterns of interaction, and reveals structural differences among the discussions about articles from different semantic areas.
You Are What You Tweet: Analyzing Twitter for Public Health
Authors: Paul Michael , Dredze Mark
Published: November 2022

Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.
Identifying Users Across Social Tagging Systems
Authors: Iofciu Tereza , Fankhauser Peter , Abel Fabian , Bischoff Kerstin
Published: November 2022

How much do tagging activities tell about a user? Is it possible to identify people in Delicious based on the tags, which they use in Flickr? In this paper we study those questions and investigate whether users can be identified across social tagging systems. We combine two kinds of information: their user ids and their tags. We introduce and compare a variety of approaches to measure the distance between user profiles for identification. With the best performing combination we achieve, depending on the actual settings, accuracies of between 60% and 80% which demonstrates that the traces of Web 2.0 users can reveal quite much about their identity.
Tracking Visual Memes in Rich-Media Social Communities
Authors: Xie Lexing , Natsev Apostol , Kender John , Hill Matthew , Smith John
Published: November 2022

We propose tools and methods to track visual memes on community-centric rich-media repositories, such as YouTube. Visual memes refer to frequently reposted short video segments. Our method can be used to monitor the reflections of real-world events in rich media, including images and videos. We first design a large-scale event-based social video collection system to continuously monitor events that unfold in real-time. We design a scalable detection algorithms that can detect visual memes with over 96\% precision and 80\% recall. Visual memes are used for various analysis such as tracking the fraction of original content, extracting the iconic picture of an event, inferring influential users in the community, and so on. We present example observations on several real-world video collections from YouTube, containing up to 1.2 million video shots.
Characterizing Social Relations Via NLP-Based Sentiment Analysis
Authors: Groh Georg , Hauffa Jan
Published: November 2022

We investigate and evaluate methods for the characterization of social relations from textual communication context, using e-mail as an example. Social relations are intrinsically characterized by the Cartesian product of weights on various axes (we employ valuation and intensity as examples). The prediction of these characteristics is performed by application of unsupervised learning algorithms on meta-data, communication statistics, and the results of deep linguistic analysis of the message body. Classification of sentiment polarity is chosen as the means of linguistic analysis. We find that prediction accuracy can be improved by introducing limited amounts of additional information.
Using Network Structure to Identify Groups in Virtual Worlds
Authors: Shah Fahad , Sukthankar Gita
Published: November 2022

Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? In this paper, we address the problem of identifying social groups from conversation data and present results of an empirical study on identifying groups in a virtual world. Virtual worlds are interesting because group membership is more shaped by common interests and less influenced by cultural and socio-economic factors. Our finding is that a combination of network measures is more predictive of group membership than language cues, and that both types of features can be combined to improve prediction.
Understanding the Demographics of Twitter Users
Authors: Mislove Alan , Lehmann Sune , Ahn Yong-Yeol , Onnela Jukka-Pekka , Rosenquist J.
Published: November 2022

Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.
Using the H-Index to Estimate Blog Authority
Authors: Devezas José , Nunes Sérgio , Ribeiro Cristina
Published: November 2022

Link analysis is a technique frequently used in the ranking of web sites. On the web, we often encounter content that is organized by entries, sorted from recent to old, and generally follows the structure of a blog. In this paper we explore and evaluate the usage of a bibliometrics measure, called h-index, for the task of blog ranking, in an information retrieval context. We base our experiments on the TREC Blogs08 collection, which comprises over 28 million posts. The results obtained indicate that the h-index is a robust metric that allows for an improved relevance discrimination between blogs, when compared to the in-degree. Additionally, tests performed using distinct versions of the post graph, indicate that this metric might tolerate a certain level of link clutter.