bluuebunny/crossref_metadata_2025 huggingface dataset:
CrowdLens: Experimenting with Crowd-Powered Recommendation and Explanation
Authors: Chang Shuo , Harper F. , He Lingfei , Terveen Loren
Published: November 2022

Recommender systems face several challenges, e.g., recommending novel and diverse items and generating helpful explanations. Where algorithms struggle, people may excel. We therefore designed CrowdLens to explore different workflows for incorporating people into the recommendation process. We did an online experiment, finding that: compared to a state-of-the-art algorithm, crowdsourcing workflows produced more diverse and novel recommendations favored by human judges;some crowdworkers produced high-quality explanations for their recommendations, and we created an accurate model for identifying high-quality explanations;volunteers from an online community generally performed better than paid crowdworkers, but appropriate algorithmic support erased this gap. We conclude by reflecting on lessons of our work for those considering a crowdsourcing approach and identifying several fundamental issues for future work.
Bad Apples Spoil the Fun: Quantifying Cheating in Online Gaming
Authors: Zuo Xiang , Gandy Clayton , Skvoretz John , Iamnitchi Adriana
Published: November 2022

Understanding and quantifying the factors that lead to cheating in society is problematic, due to the inherent desire to hide socially unacceptable actions. While significant progress has been made in the understanding of unethical behavior via in-lab experiments, little was measured at scale, in the wild. By analyzing cheating in online games, this paper verifies at scale and in the wild a number of previous observations drew from controlled, in-lab experiments. We verify empirically that cheating behavior is contagious, and identify some of the factors that encourage cheating and some that limit it.
Understanding Communities via Hashtag Engagement: A Clustering Based Approach
Authors: DeMasi Orianna , Mason Douglas , Ma Jeff
Published: November 2022

We develop insight into community use of hashtags on social media and find that hashtags with behavior indicative of real world communities are more engaging. To do this, we study the relationship of hashtag usage with user engagement on Twitter. Hashtag engagement is useful as a surrogate measure of how active community members are. We develop a framework for describing hashtag temporal usage, show the existence of 4 broad classes of hashtags, and show that the engagement of a hashtag varies significantly between classes. Periodically used hashtags, such as for TV shows and weekly community chats, are the most engaging, while hashtags relating to events are the least engaging. Looking at how community dynamics vary within this framework reveals that a hashtag being used more frequently is not positively correlated with it being more engaging. We then explore the periodically used hashtags and find negative correlations with diversity of the user base, which implies concentrated communities are the most engaging. We conclude by studying a set of community conversation-oriented hashtags and find these hashtags to be more engaging than other hashtags, regardless of dynamic type. Our findings support the hypothesis that hashtags with stronger community behavior are more engaging.
User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest
Authors: Newell Edward , Jurgens David , Saleem Haji , Vala Hardik , Sassine Jad , Armstrong Caitrin , Ruths Derek
Published: November 2022

Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or “mainstream” topics, but Reddit’s large userbase provides a key advantage in supporting niche topics.
Social Media Participation in an Activist Movement for Racial Equality
Authors: De Choudhury Munmun , Jhaver Shagun , Sugar Benjamin , Weber Ingmar
Published: November 2022

From the Arab Spring to the Occupy Movement, social media has been instrumental in driving and supporting socio-political movements throughout the world. In this paper, we present one of the first social media investigations of an activist movement around racial discrimination and police violence, known as “Black Lives Matter”. Considering Twitter as a sensor for the broader community’s perception of the events related to the movement, we study participation over time, the geographical differences in this participation, and its relationship to protests that unfolded on the ground. We find evidence for continued participation across four temporally separated events related to the movement, with notable changes in engagement and language over time. We also find that participants from regions of historically high rates of black victimization due to police violence tend to express greater negativity and make more references to loss of life. Finally, we observe that social media attributes of affect, behavior and language can predict future protest participation on the ground. We discuss the role of social media in enabling collective action around this unique movement and how social media platforms may help understand perceptions on a socially contested and sensitive issue like race.
Two Tales of the World: Comparison of Widely Used World News Datasets GDELT and EventRegistry
Authors: Kwak Haewoon , An Jisun
Published: November 2022

In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets. In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.
Automatic Summarization of Real World Events Using Twitter
Authors: Alsaedi Nasser , Burnap Pete , Rana Omer
Published: November 2022

Microblogging sites, such as Twitter, have become increasingly popular in recent years for reporting details of real world events via the Web. Smartphone apps enable people to communicate with a global audience to express their opinion and commentate on ongoing situations - often while geographically proximal to the event. Due to the heterogeneity and scale of the data and the fact that some messages are more salient than others for the purposes of understanding any risk to human safety and managing any disruption caused by events, automatic summarization of event-related microblogs is a non-trivial and important problem. In this paper we tackle the task of automatic summarization of Twitter posts, and present three methods that produce summaries by selecting the most representative posts from real-world tweet-event clusters. To evaluate our approaches, we compare them to the state-of-the-art summarization systems and human generated summaries. Our results show that our proposed methods outperform all the other summarization systems for English and non-English corpora.
Identifying Rhetorical Questions in Social Media
Authors: Ranganath Suhas , Hu Xia , Tang Jiliang , Wang Suhang , Liu Huan
Published: November 2022

Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this paper, we develop a framework to identify rhetorical questions by modeling the motivations of the users to post them. We focus on one motivation of the users drawing from linguistic theories, to implicitly convey a message. We develop a framework from this motivation to identify rhetorical questions in social media and evaluate the framework using questions posted on Twitter. This is the first framework to model the motivations for posting rhetorical questions to identify them on social media platforms.
Evaluating Public Response to the Boston Marathon Bombing and Other Acts of Terrorism through Twitter
Authors: Buntain Cody , Golbeck Jennifer , Liu Brooke , LaFree Gary
Published: November 2022

Twitter and other social platforms have become important communication channels during crises. While research into crisis informatics and social media is growing, the rarity of terrorist attacks in developed, Western countries complicates analysis of these specific events. To address this gap, we explore social media response to three terror events: the 2013 Boston Marathon Bombing, the 2014 Sydney Hostage Crisis, and the 2015 Charlie Hebdo Shooting. We show that, while these events do not significantly impact general Twitter usage, those users who are discussing the event behave in predictable ways across all three events. Such behaviors include increased references to the event and use of retweets, hashtags, and URLs. Furthermore, local news affiliates and law enforcement agencies (if present on social media) emerge as central actors in the networks.
Community Cores: Removing Size Bias from Community Detection
Authors: Jones Isaac , Wang Ran , Han Jiawei , Liu Huan
Published: November 2022

Community discovery in social networks has received a significant amount of attention in the social me- dia research community. The techniques developed by the community have become quite adept at identifying the large communities in a network, but often neglect smaller communities. Evaluation techniques also show this bias, as the resolution limit problem in modular- ity indicates. Small communities, however, account for a higher proportion of a social network’s community membership and reveal important information about the members of these communities. In this work, we intro- duce a re-weighting method to improve both the over- all performance of community detection algorithms and performance on small community detection.
A Data-Driven Study of View Duration on YouTube
Authors: Park Minsu , Naaman Mor , Berger Jonah
Published: November 2022

Video watching had emerged as one of the most frequent media activities on the Internet. Yet, little is known about how users watch online video. Using two distinct YouTube datasets, a set of random YouTube videos crawled from the Web and a set of videos watched by participants tracked by a Chrome extension, we examine whether and how indicators of collective preferences and reactions are associated with view duration of videos. We show that video view duration is positively associated with the video's view count, the number of likes per view, and the negative sentiment in the comments. These metrics and reactions have a significant predictive power over the duration the video is watched by individuals. Our findings provide a more precise understandings of user engagement with video content in social media beyond view count.
Characterizing Deleted Tweets and Their Authors
Authors: Bhattacharya Parantapa , Ganguly Niloy
Published: November 2022

This paper provides a detailed characterization of tweets posted and then later deleted by their authors, in the Twitter microblogging platform. Our characterization shows significant personality differences between users who delete their tweets and those who do not. We find that users who delete their tweets are more likely to be extroverted and neurotic while being less conscientious. Further, although deleted tweets contain more negative sentiment and swear words, they also show significant signs of being thoughtfully constructed.
Predicting Group Success in Meetup
Authors: Pramanik Soumajit , Gundapuneni Midhun , Pathak Sayan , Mitra Bivas
Published: November 2022

Success of groups in Meetup is of utmost importance for members who organize them. However, measures of group success in Meetup is quite vague till now. In this paper, we take a step to quantify the success of Meetup groups. Driven by a comprehensive study of our Meetup dataset, we handpick a set of key properties which can potentially regulate a group’s success. Finally, we develop a machine learning model leveraging on these features which can predict success of Meetup groups early with high accuracy.
Tracking the Yak: An Empirical Study of Yik Yak
Authors: Saveski Martin , Chou Sophie , Roy Deb
Published: November 2022

To investigate the effects of anonymity on user behavior, we conduct an empirical study of the new and controversial social app, Yik Yak. First, we examine how users use the platform, analyzing patterns in posting, popularity of yaks, and vocabulary. As a comparison, we look at posting patterns on Twitter, which has similar limitations on lengths of posts, but is public and global rather than anonymous and local. Upon a sample of 2.9M posts (1.9M yaks and 1M geotagged tweets) from 20 locations across the USA, we find that interactions on Yik Yak are specific to its location limitations and reflect the schedules of its targeted demographic, college students. Second, we test two hypotheses related to anonymity and communication: (i) whether vulgarity usage is more likely to be acceptable, and (ii) whether unique topics emerge in conversations on Yik Yak. We find that posts on Yik Yak are only slightly more likely to contain vulgarities, and we do not find any significant bias in topic distributions on Yik Yak versus on Twitter; however, differences in vocabulary and most discriminative words used suggest the need for further analysis.
Automatic Detection and Categorization of Election-Related Tweets
Authors: Vijayaraghavan Prashanth , Vosoughi Soroush , Roy Deb
Published: November 2022

With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign.However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90.
Topic Modeling in Twitter: Aggregating Tweets by Conversations
Authors: Alvarez-Melis David , Saveski Martin
Published: November 2022

We propose a new pooling technique for topic modeling in Twitter, which groups together tweets occurring in the same user-to-user conversation. Under this scheme, tweets and their replies are aggregated into a single document and the users who posted them are considered co-authors. To compare this new scheme against existing ones, we train topic models using Latent Dirichlet Allocation (LDA) and the Author-Topic Model (ATM) on datasets consisting of tweets pooled according to the different methods. Using the underlying categories of the tweets in this dataset as a noisy ground truth, we show that this new technique outperforms other pooling methods in terms of clustering quality and document retrieval.
Tweet Acts: A Speech Act Classifier for Twitter
Authors: Vosoughi Soroush , Roy Deb
Published: November 2022

Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than 0.70. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.
Studying Multicultural Diversity of Cities and Neighborhoods through Social Media Language Detection
Authors: Arnaboldi Michela , Brambilla Marco , Cassottana Beatrice , Ciuccarelli Paolo , Ripamonti Davide , Vantini Simone , Volonterio Riccardo
Published: November 2022

Cities are growing as melting pots of people with different culture, religion, and language. In this paper, through multilingual analysis of Twitter contents shared within a city, we analyze the prevalent language in the different neighborhoods of the city and we compare the results with census data, in order to highlight any parallelisms or discrepancies between the two data sources. We show that the officially identified neighborhoods are actually representing significantly different communities and that the use of the social media as a data source helps to detect those weak signals that are not captured from traditional data.
This Is Not Just a Café: Toward Capturing the Dynamics of Urban Places
Authors: Lee Myeong , Farzan Rosta , Butler Brian
Published: November 2022

Social media has provided a huge amount of user-generated data in capturing urban dynamics. Among them, place-level human behavior has been largely detected through people’s check-in records at certain places. Conventionally, places are characterized by a set of pre-defined features, often specified by the owner of the places. In this paper, we argue that capturing socially-meaningful features and dynamics of an urban place may also be done by analyzing human activity traces. We explore the activity-centered representation of urban places as a foundation for understanding local neighborhoods at scale. We analyze activities from several venues using data from MeetUp, a popular local event organizing service. Then, Yelp’s business information was used to triangulate the analysis. The results suggest that strategies based on local event data have great potential for modeling socially-meaningful urban places at scale.
Information Laundering and Counter-Publics: The News Sources of Islamophobic Groups on Twitter
Authors: Puschmann Cornelius , Ausserhofer Julian , Maan Noura , Hametner Markus
Published: November 2022

Which news sources do supporters of populist islamophobic groups and their opponents rely on, and how are these sources related to each other? We explore these questions by studying the websites referenced in discussions sur- rounding Pegida, a right-wing populist movement based in Germany that is opposed to what its supporters regard as is- lamization, cultural marginalization and political correct- ness. We draw on a manual content analysis of the news sources and the stances of Twitter users, to then calculate the overlap of sources across audiences. Finally, we perform a cluster analysis of the resulting user groups, based on shared sources. Preferences by language, nationality, region and politics emerge, showing the distinction between differ- ent groups among the users. Our tentative findings have im- plications both for the study of mass media audiences through the lens of social media, and for research on the public sphere and its possible fragmentation in online dis- course. This contribution, which is the result of an interdis- ciplinary collaboration between communication scholars in Germany and journalists in Austria, is part of a larger ongo- ing effort to understand forms of online extremism through the analysis of social media data.
Model Driven Development of Social Media Environmental Monitoring Applications
Authors: Brambilla Marco , Mauri Andrea , Umuhoza Eric
Published: November 2022

The growth of social media and the resulting increase of user generated content attracted people coming from different fields interested in analyzing this kind of data. Recently user generated content has been used for environmental monitoring. In this paper, we propose a model-driven approach for developing social media applications for environmental monitoring that will provide a simplified solution for integrating various data sources, exploiting different platforms and allowing complex data analysis to be performed.
Climate Change Engagement: Results of a Multi-Task Game with a Purpose
Authors: Piccolo Lara , Fernandez Miriam , Alani Harith , Scharl Arno , Föls Michael , Herring David
Published: November 2022

Addressing the challenge of engaging people with climate change, this paper sheds light on the Climate Challenge, a crowdsourcing application in the tradition of games with purpose that relies on different strategies for informing and inviting users to adopt sustainable lifestyle choices. Towards building an extensive perspective of engagement, we statistically analyse specific game strategies based on users’ participation and performance, and build a panorama of users’ positioning in a behaviour change process. Preliminary results suggest features that should be considered in a Climate Challenge design roadmap.
Fashion Conversation Data on Instagram
Authors: Ha Yu-i , Kwon Sejeong , Cha Meeyoung , Joo Jungseock
Published: November 2022

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community.
Visualizing Health Awareness in the Middle East
Authors: Araujo Matheus , Mejova Yelena , Aupetit Michael , Weber Ingmar
Published: November 2022

We present an interactive tool that visualizes data on awareness of several health conditions in the Middle East. The underlying data is obtained via Facebook's Marketing API and includes rich demographic details. We discuss how this tool may be useful for planning more targeted public health campaigns and for monitoring campaign effectiveness.
Changes in Social Media Affect, Disclosure, and Sociality for a Sample of Transgender Americans in 2016’s Political Climate
Authors: Haimson Oliver , Hayes Gillian
Published: November 2022

In the wake of 2016’s divisive political climate in the US, media reports indicated that vulnerable people, such as the transgender population, may be experiencing lower than normal rates of emotional wellbeing. To test these claims, we analyzed social media linguistic markers of affect, disclosure, and sociality in late 2016 as compared to the same month a year prior in a sample of US Tumblr blogs documenting people’s gender transitions. We find that both negative and positive affect, and words related to anger in particular, increased for trans women in 2016. At the same time, social words used to describe family and friends decreased for trans people, indicating that they may have interacted less with family and friends in late 2016. Self-disclosure decreased in 2016, potentially indicating increased political language vs. personal content, or self-censorship in response to a hostile political environment. Results highlight ways large scale external political events may impact how people communicate and disclose on social media. Additionally, our results indicate that social media data could also be used to identify those most in need of mental wellbeing resources in response to a hostile political climate.
Cold Hard E-Cash: Friends and Vendors in the Venmo Digital Payments System
Authors: Zhang Xinyi , Tang Shiliang , Zhao Yun , Wang Gang , Zheng Haitao , Zhao Ben
Published: November 2022

For millions around the globe, digital payment apps such as Venmo are replacing cash as the preferred method of payment between friends and vendors. Apps like Venmo bring a unique blend of convenience and social interactions into financial transactions. In this paper, we study the role of social relationships in the adoption of the Venmo digital payment system. We collect records of all 91 million public transactions conducted on Venmo since its introduction, a social graph connecting most of its 10.5 million users, and analyze the interplay between social relationships and financial transactions. We find that Venmo communities are very densely connected compared to other interaction networks, and are often driven by specific niche applications. We are able to extract both user-to-user and user-to-vendor transaction communities, and show that they exhibit dramatically different structural properties.
Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter Across Languages
Authors: Volkova Svitlana , Bell Eric
Published: November 2022

Social networks have an ephemerality to them where accounts and messages are constantly being edited, deleted, or marked as private. This continuous change comes from concerns around privacy, a potential desire for to be forgotten and suspicious behavior. In this study we present a novel task – predicting suspicious e.g., to be deleted or suspended accounts in social media. We analyze multiple datasets of thousands of active, deleted and suspended Twitter accounts to produce a series of predictive representations for the removal or shutdown of an account. We selected these accounts from speakers of three languages – Russian, Spanish, and English to evaluate if speakers of various languages behave differently with regards to deleting accounts. We compared the predictive power of the state-of-the-art machine learning models to recurrent neutral networks trained on previously unexplored features. Furthermore, this work is the first to rely on image and affect signals in addition to language and network to predict deleted and suspended accounts in social media. We found that unlike widely used profile and network features, the discourse of deleted or suspended versus active accounts forms the basis for highly accurate account deletion and suspension prediction. More precisely, we observed that the presence of certain terms in tweets leads to a higher likelihood for that user’s account deletion or suspension. Moreover, despite image and affect signals yield lower predictive performance compared to language, they reveal interesting behavioral differences across speakers of different languages. Our extensive analysis and novel findings on language use and suspicious behavior of speakers of different languages can improve the existing approaches to credibility analysis, disinformation and deception detection in social media.
Which Size Matters? Effects of Crowd Size on Solution Quality in Big Data Q&A Communities
Authors: Tausczik Yla , Wang Ping , Choi Joohee
Published: November 2022

Question Answering websites have evolved into one of the most important platforms for knowledge sharing and problem solving online. Despite widespread adoption of Q&As by technical communities as well as an abundance of domain experts, many questions fail to attract a sufficient audience to obtain a good solution or any solution at all. We investigate the effects of crowd size on solution quality in Stack Exchange Q&A communities on topics related to big data. We find that three distinct levels of group size in the crowd (topic audience size, question audience size, and number of contributors) affect solution quality. Therefore, we argue that group size in the crowd is not unitary, but rather a multi-level construct. This work advances a theoretical model of group size in the crowd and the relation between crowd size and performance. The work also has practical implications for system designers trying to route crowds to problems efficiently.
Characterizing Online Discussion Using Coarse Discourse Sequences
Authors: Zhang Amy , Culbertson Bryan , Paritosh Praveen
Published: November 2022

In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.
Adaptive Spammer Detection with Sparse Group Modeling
Authors: Wu Liang , Hu Xia , Morstatter Fred , Liu Huan
Published: November 2022

Social spammers disseminate unsolicited information on social media sites that negatively impacts social networking systems. To detect social spammers, traditional methods leverage social network structures to identify the behavioral patterns hidden in their social interactions. They focus on accounts that are affiliated with groups comprising known spammers. However, since different parties are emerging to generate various spammers, they may form different kinds of groups, and some spammers may even detach from the flock. Therefore, it is challenging for existing methods to find the optimal group structure that captures different spammers simultaneously. Employing different approaches for specific spammers is time-consuming, and it also lacks the adaptivity of dealing with emerging spammers. In this work, we aim to propose a group modeling framework that adaptively characterizes social interactions of spammers. In particular, we introduce to integrate content information into the group modeling process. The proposed framework exploits additional content information in selecting groups and individuals that are likely to be involved in spamming activities. In order to alleviate the intensive computational cost, we transform the problem as a sparse learning task that can be solved efficiently. Experimental results on real-world datasets show that the proposed method outperforms the state-of-the-art approaches.
The Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk
Authors: De Choudhury Munmun , Kiciman Emre
Published: November 2022

Online social support is known to play a significant role in mental well-being. However, current research is limited in its ability to quantify this link. Challenges exist due to the paucity of longitudinal, pre- and post mental illness risk data, and reliable methods that can examine causality between past availability of support and future risk. In this paper, we propose a method to measure how the language of comments in Reddit mental health communities influences risk to suicidal ideation in the future. Incorporating human assessments in a stratified propensity score analysis based framework, we identify comparable subpopulations of individuals and measure the effect of online social support language. We interpret these linguistic cues with an established theoretical model of social support, and find that esteem and network support play a more prominent role in reducing forthcoming risk. We discuss the implications of our work for designing tools that can improve support provisions in online communities.
The Power of the Patient Voice: Learning Indicators of Treatment Adherence From An Online Breast Cancer Forum
Authors: Yin Zhijun , Malin Bradley , Warner Jeremy , Hsueh Pei-Yun , Chen Ching-Hua
Published: November 2022

Social media platforms have become popular online environments for patients seeking and sharing treatment experiences. These platforms enable us to move beyond traditional sources of clinical information for learning about a patient's long-term adherence to treatment. While adherence has been studied using data derived from medical records and structured surveys, these approaches are limited in that they are often 1) time consuming, 2) limited in scale, or 3) lack self-reported patient experiences. In this paper, we investigate treatment adherence through a patient's self-reported information in online discussion forums. Specifically, we consider hormonal therapy treatment adherence (HTA) for hormone receptor positive breast cancer, a disease subtype that comprises 75% of all breast cancer cases. We focus on the inferred emotions and personality traits from the posts created by the members of a large online breast cancer community. These factors have been neglected in traditional adherence research due to a lack of information. We study over 130,000 posts from the forum, spanning 10,000 patients over 9 years. We assess emotion and personality traits with respect to three types of adherence behaviors: 1) currently on a regimen, 2) an interruption (due to discontinuing, pausing, or switching a medication regimen before five years) and 3) the completion of a five-year protocol. We find statistically significant differences in emotions across adherence behaviors. We further show that specific personality traits, including self-discipline, are associated with HTA, but in the opposite direction than what traditional research studies have shown. Finally, we illustrate that there is potential for predicting future interruption behaviors based on an individual's posts. We anticipate that our methodology can be applied to study treatment adherence for other diseases using online self-reported information.
“Be Careful; Things Can Be Worse than They Appear”: Understanding Biased Algorithms and Users’ Behavior Around Them in Rating Platforms
Authors: Eslami Motahhare , Vaccaro Kristen , Karahalios Karrie , Hamilton Kevin
Published: November 2022

Awareness of bias in algorithms is growing among scholars and users of algorithmic systems. But what can we observe about how users discover and behave around such biases? We used a cross-platform audit technique that analyzed online ratings of 803 hotels across three hotel rating platforms and found that one site’s algorithmic rating system biased ratings, particularly low-to-medium quality hotels, significantly higher than others (up to 37%). Analyzing reviews of 162 users who independently discovered this bias, we seek to understand if, how, and in what ways users perceive and manage this bias. Users changed the typical ways they used a review on a hotel rating platform to instead discuss the rating system itself and raise other users’ awareness of the rating bias. This raising of awareness included practices like efforts to reverse-engineer the rating algorithm, efforts to correct the bias, and demonstrations of broken trust. We conclude with a discussion of how such behavior patterns might inform design approaches that anticipate unexpected bias and provide reliable means for meaningful bias discovery and response.
Cultural Diffusion and Trends in Facebook Photographs
Authors: You Quanzeng , García-García Darío , Paluri Mahohar , Luo Jiebo , Joo Jungseock
Published: November 2022

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user's photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion.
Exploiting Contextual Information for Fine-Grained Tweet Geolocation
Authors: Chong Wen-Haw , Lim Ee-Peng
Published: November 2022

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we also utilize the properties of (1) spatial focus where users are more likely to visit venues near each other and (2) spatial homophily where venues near each other tend to share more similar tweet content, compared to venues further apart. Our proposed model significantly outperforms the content-only approaches.
A Gendered Analysis of Leadership in Enterprise Social Networks
Authors: Di Tommaso Giorgia , Stilo Giovanni , Velardi Paola
Published: November 2022

The present study is concerned with the analysis of women’s leadership in a less formal work environment, such as an enterprise social network. Our aim is to answer the following research questions: RQ1: Are Enterprise Social Networks a conductive environment to support the emergence of women informal leadership? RQ2: If answer to RQ1 is positive, do women actually exploit this opportunity?
US Presidential Election: What Engaged People on Facebook
Authors: Kharratzadeh Milad , Ustebay Deniz
Published: November 2022

We study Facebook posts published by major news organizations in the 10-month period leading to the 2016 presidential election. Our goal is to explore the topics related to the two major party candidates, Hillary Clinton and Donald Trump, and identify the ones that engaged the Facebook users the most. The engagement is measured by the total number of reactions, comments, and shares. Using topic modeling with Linear Dirichlet Allocation (LDA) on the Facebook posts, we identify the top 10 topics related to each candidate and then assess the audience engagement for these topics across 10 different news organizations. We use Hierarchical Bayesian Models (HBMs) to analyze the data, which allow us to partially pool the information across different sources.
On the Interpretability of Thresholded Social Networks
Authors: Tsur Oren , Lazer David
Published: November 2022

Understanding the factors of network formation is a fundamental aspect in the study of social dynamics. Online activity provides us with abundance of data that allows us to reconstruct and study social networks. Statistical inference methods are often used to study network formation. Ideally, statistical inference allows the researcher to study the significance of specific factors to the network formation. One popular framework is known as Exponential Random Graph Models (ERGM) which provides principled and statistically sound interpretation of an observed network structure. Network, however, are not always given set in stone. Often times, a network is “reconstructed” by applying some thresholds on the observed data/signals. We show that subtle changes in the thresholding have significant effects on the ERGM results, casting doubts on the interpretability of the model. In this work we present a case study in which different thresholding techniques yield radically different results that lead to contrastive interpretations. Consequently, we revisit the applicability of ERGM to thresholded networks.
Identifying Leading Indicators of Product Recalls from Online Reviews Using Positive Unlabeled Learning and Domain Adaptation
Authors: Bhat Shreesh , Culotta Aron
Published: November 2022

Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to an online government portal. We find that our approach results in an absolute F1 score improvement of 8% over the best competing baseline. Furthermore, when we apply the classifier to Amazon reviews of known recalled products, we identify safety hazard reports prior to the recall date for 45% of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced.
A Longitudinal Study of Topic Classification on Twitter
Authors: Iman Zahra , Sanner Scott , Bouadjenek Mohamed Reda , Xie Lexing
Published: November 2022

Twitter represents a massively distributed information source over a kaleidoscope of topics ranging from social and political events to entertainment and sports news. While recent work has suggested that variations on standard classifiers can be effectively trained as topical filters (Lin, Snow, and Morgan 2011; Yang et al. 2014; Magdy and Elsayed 2014), there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? Furthermore, what features and feature classes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that (1) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training and (2) simple terms and locations are the most informative feature classes (despite training on classes labeled via hashtags).
Detecting Socio-Economic Impact of Cultural Investment Through Geo-Social Network Analysis
Authors: Zhou Xiao , Hristova Desislava , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

Taking advantage of nearly 4 million transition records for three years in London from a popular location-based social network service, Foursquare, we study how to track the impact and measure the effectiveness of cultural investment in small urban areas. We reveal the underlying relationships between socio-economic status, local cultural expenditure, and network features extracted from user mobility trajectories. This research presents how geo-social and mobile services more generally can be used as a proxy to track local changes as government financial effort is put in developing urban areas, and thus gives evidence and suggestions for further policy-making and investment optimization.
On Quitting: Performance and Practice in Online Game Play
Authors: Agarwal Tushar , Burghardt Keith , Lerman Kristina
Published: November 2022

We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, that is, sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and “grit:” successful players are those who persist in their practice despite lower scores. Finally, we train an ε-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game. Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.