bluuebunny/crossref_metadata_2025 huggingface dataset:
Are There Gender Differences in Professional Self-Promotion? An Empirical Case Study of LinkedIn Profiles Among Recent MBA Graduates
Authors: Altenburger Kristen , De Rajlakshmi , Frazier Kaylyn , Avteniev Nikolai , Hamilton Jim
Published: November 2022

Women are more modest than men in expressing accomplishments, referred to as the “feminine modesty effect”. Given the importance of highlighting accomplishments and skills for professional advancement, our research revisits the classical question of equal opportunity with a modern dataset to examine how women leverage LinkedIn, a professional social networking site. We first apply propensity score matching methods to identify a subset of similarly qualified female and male U.S. users who recently graduated/will be graduating (2011-2017) from a top-ranked MBA program as indicated on their LinkedIn profile. We then analyze gender differences in online self-promotion choices, an often overlooked aspect of understanding the role of gender in the professional hiring pipeline. Among matched subsets of female and male users, we find that females are less likely relative to males to utilize data fields that require writing in free-form such as the Summary and Job Description fields. However, we find for most universities that females and males are equally likely to include more structured data fields such as Honors and Skills, and for some universities females are more likely to include at least one Skill. This work begins to quantify gender biases in user-provided data and introduces important considerations for how self-presentation choices affect professional opportunity in online hiring platforms.
Inverse Dynamical Inheritance in Stack Exchange Taxonomies
Authors: Ojeda César , Cvejoski Kostadin , Sifa Rafet , Bauckhage Christian
Published: November 2022

Question Answering websites are popular repositories of expert knowledge and cover areas as diverse as linguistics, computer science, or mathematics. Knowledge is commonly organized via user defined tags which implicitly create population folksonomies. However, the interplay between latent knowledge structures and the answering behavior of users has not been fully explored yet. Here, we propose a model of a dynamical tagging process guided by taxonomies, devise a robust algorithm that allow us to uncover hidden topic hierarchies, apply our method to analyze several Stack Exchange websites. Our results show that the dynamics of the system strongly correlate with uncovered taxonomies.
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Authors: Maity Suman , Kharb Aman , Mukherjee Animesh
Published: November 2022

Quora is one of the most popular community Q and A sites of recent times. However, many question posts on this Q and A site often do not get answered. In this paper, we quantify various linguistic activities that discriminates an answered question from an unanswered one. Our central finding is that the way users use language while writing the question text can be a very effective means to characterize answerability. This characterization helps us to predict early if a question remaining unanswered for a specific time period t will eventually be answered or not and achieve an accuracy of 76.26% (t=1 month) and 68.33% (t=3 months). Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74.18%. We also compare our method with some of the similar works (Dror et al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.
Two-Phase Influence Maximization in Social Networks with Seed Nodes and Referral Incentives
Authors: Mondal Sneha , Dhamal Swapnil , Narahari Y.
Published: November 2022

The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most solution approaches available in the existing literature devote the entire budget towards triggering diffusion at seed nodes. This paper investigates the effect of splitting the budget across two different, sequential phases. In phase 1, we adopt the classical approach of initiating diffusion at a selected seed-set. In phase 2, we use the remaining budget to offer referral incentives. We formulate this problem and explore suitable ways to split the budget between the two phases, with detailed experiments on synthetic and real-world datasets. The principal findings from our study are: (a) when the budget is low, it is prudent to use the entire budget for phase 1; (b) when the budget is moderate to high, it is preferable to use much of the budget for phase 1, while allocating the remaining budget to phase 2; (c) in the presence of moderate to strict temporal constraints, phase 2 is not warranted; (d) if the temporal constraints are low or absent, phase 2 yields a decisive improvement in influence spread.
A Motif-Based Approach for Identifying Controversy
Authors: Coletto Mauro , Garimella Kiran , Gionis Aristides , Lucchese Claudio
Published: November 2022

Among the topics discussed in Social Media, some lead to controversy. A number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, and of investigating the global network structure. In this work we show that it is possible to detect controversy in social media by exploiting network motifs, that is, local patterns of user interaction. The proposed approach allows for a language-independent and fine-grained and efficient-to-compute analysis of user discussions and their evolution over time. The supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features.
Robust Classification of Crisis-Related Data on Social Networks Using Convolutional Neural Networks
Authors: Nguyen Dat , Ali Al Mannai Kamela , Joty Shafiq , Sajjad Hassan , Imran Muhammad , Mitra Prasenjit
Published: November 2022

The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The scarcity of labeled data, particularly in the early hours of a crisis, delays the learning process. Existing classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for identifying useful tweets during a crisis situation. At the onset of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results.
Headlines Matter: Using Headlines to Predict the Popularity of News Articles on Twitter and Facebook
Authors: Piotrkowicz Alicja , Dimitrova Vania , Otterbacher Jahna , Markert Katja
Published: November 2022

Social media like Facebook or Twitter have become an entry point to news for many readers. In that scenario, the headline is the most prominent — and often the only visible — part of the news article. We propose a novel task of using only headlines to predict the popularity of news articles. The prediction model is evaluated on headlines from two major broadsheet news outlets — The Guardian and New York Times. We significantly improve over several baselines, noting differences in the model performance between Facebook and Twitter.
Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond
Authors: Kaghazgaran Parisa , Caverlee James , Alfifi Majid
Published: November 2022

We exploit the prevalence of malicious review writers on crowdsourcing platforms like RapidWorkers to identify actual fraud reviews on Amazon. Complementary to previous efforts which often rely on proxies for fraud reviews, we present a long-term study of actual fraudulent behaviors in online review manipulation. We find that these malicious reviewers — though often providing seemingly legitimate opinions — do exhibit significant differences from normal reviewers in terms of ratings distribution, length of the reviews, and the burstiness of the reviews themselves. We additionally study the evolution of these reviews, and find striking temporal changes that could support future discovery of these reviewers who may be “hiding in plain sight.”
Scalable News Slant Measurement Using Twitter
Authors: Le Huyen , Shafiq Zubair , Srinivasan Padmini
Published: November 2022

Prior research has reported evidence of polarization in the consumption and distribution of political news in online social media. Methods to effectively measure slant of news articles at a large scale are lacking in prior literature. To fill this gap, we propose a method to measure slant of individual news articles by observing their sharing patterns on Twitter. Our method monitors tweets about a news article and a set of landmark Democrat and Republican accounts on Twitter to estimate its slant. Our results show that the slant of news articles estimated by our method matches the slant estimated by crowd sourced workers.
Modeling of Political Discourse Framing on Twitter
Authors: Johnson Kristen , Jin Di , Goldwasser Dan
Published: November 2022

Framing is a political strategy in which politicians carefully word their statements in order to control public perception and discussion of current issues. Previous works exploring political framing have focused on analysis of frames in longer texts, such as newspaper articles, or tweets relevant to specific events. We present the first in-depth analysis of issue-independent framing for political discourse in social media, specifically the microblogging platform Twitter. Building upon the fifteen frames designed by Boydstun, we propose three additional frames relevant to Twitter and provide insights into the dynamic usage of frames by party and over time. Finally we present a global probabilistic model for combining linguistic, issue, and party bias features of the tweets of politicians for the task of tweet frame prediction.
Automatically Identifying Good Conversations Online (Yes, They Do Exist!)
Authors: Napoles Courtney , Pappu Aasish , Tetreault Joel
Published: November 2022

Online news platforms curate high-quality content for their readers and, in many cases, users can post comments in response. While comment threads routinely contain unproductive banter, insults, or users “shouting” over each other, there are often good discussions buried among the noise. In this paper, we define a new task of identifying “good” conversations, which we call ERICs — Engaging, Respectful, and/or Informative Conversations. Our model successfully identifies ERICs posted in response to online news articles with F1 = 0.73 and F1 = 0.91 in debate forums.
Controlling for Unobserved Confounds in Classification Using Correlational Constraints
Authors: Landeiro Virgile , Culotta Aron
Published: November 2022

As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. In this paper, we consider the case where there is an unobserved confounding variable z that influences both the features x and the class variable y. When the influence of z changes from training to testing data, we find that the classifier accuracy can degrade rapidly. In our approach, we assume that we can predict the value of z at training time with some error. The prediction for z is then fed to Pearl's back-door adjustment to build our model. Because of the attenuation bias caused by measurement error in z, standard approaches to controlling for z are ineffective. In response, we propose a method to properly control for the influence of z by first estimating its relationship with the class variable y, then updating predictions for z to match that estimated relationship. By adjusting the influence of z, we show that we can build a model that exceeds competing baselines on accuracy as well as on robustness over a range of confounding relationships.
Spam Users Identification in Wikipedia Via Editing Behavior
Authors: Green Thomas , Spezzano Francesca
Published: November 2022

In this paper, we address the problem of identifying spam users on Wikipedia and present our preliminary results. We formulate the problem as a binary classification task and propose a set of features based on user editing behavior to separate spammers from benign users. We tested our system on a new dataset we built consisting of 4.2K (half spam and half benign) users and 75.6K edits. Experimental results show that our approach reaches 80.8% classification accuracy and 0.88 mean average precision. We compared against ORES, the most recent tool developed by Wikimedia which assigns a damaging score to each edit, and we show that our system outperforms ORES in spam users detection. Moreover, by combining our features with ORES, classification accuracy increases to 82.1%. Additionally, we also show that our system performs well in a more realistic, unbalanced setting, that is, when spammers are greatly outnumbered by benign users, by achieving an AUROC of 0.84 (which increases to 0.86 when we combine with ORES).
The Impact of Crowds On News Engagement: A Reddit Case Study
Authors: Horne Benjamin , Adali Sibel
Published: November 2022

Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on reddit.com, asocial platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on Reddit is predictable using previously studied sentiment and content features and that posts with titles changed by Reddit users tend to be more popular than posts with the original article title.
Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'
Authors: Wang Yu , Zhang Xiyang , Luo Jiebo
Published: November 2022

We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the candidates and second, using negative binomial regression and exploiting the variations in `likes,' we evaluate the effectiveness of these tactics. Thirdly, we rank the candidates' campaign tactics by calculating the conditional expectation of their generated 'likes.' We show that while Ted Cruz and Marco Rubio put much weight on President Obama, this tactic is not being well received by their supporters. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates and that the women issue is equally emphasized in Sanders' campaign as in Clinton's. Finally, we suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) prototype policies and test the public response from the social media.
The Impact of News Values and Linguistic Style on the Popularity of Headlines On Twitter and Facebook
Authors: Piotrkowicz Alicja , Dimitrova Vania , Otterbacher Jahna , Markert Katja
Published: November 2022

A large proportion of audiences read news online, often accessing news articles through social media like Facebook or Twitter. A distinguishing characteristic of news on social media is that the most prominent (and often the only visible) part of the news article is the headline. We investigate the impact of headline characteristics, including journalistic concepts of news values and linguistic style, on the article's social media popularity. Using a large corpus of headlines from The Guardian and New York Times we derive these features automatically and correlate with social media popularity on Twitter and Facebook. We found most of them to have a significant effect and that their impact differs between the two social media and between news outlets. Further investigation with a crowdsourced study confirms that news values and style influence the audiences' decisions to click on a headline.
LearningQ: A Large-Scale Dataset for Educational Question Generation
Authors: Chen Guanliang , Yang Jie , Hauff Claudia , Houben Geert-Jan
Published: November 2022

We present LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. We show that, compared to existing datasets that can be used to generate educational questions, LearningQ (i) covers a wide range of educational topics and (ii) contains long and cognitively demanding documents for which question generation requires reasoning over the relationships between sentences and paragraphs. As a result, a significant percentage of LearningQ questions (~30%) require higher-order cognitive skills to solve (such as applying, analyzing), in contrast to existing question-generation datasets that are designed mostly for the lowest cognitive skill level (i.e. remembering). To understand the effectiveness of existing question generation methods in producing educational questions, we evaluate both rule-based and deep neural network based methods on LearningQ. Extensive experiments show that state-of-the-art methods which perform well on existing datasets cannot generate useful educational questions. This implies that LearningQ is a challenging test bed for the generation of high-quality educational questions and worth further investigation. We open-source the dataset and our codes at https://dataverse.mpi-sws.org/dataverse/icwsm18.
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
Authors: Founta Antigoni , Djouvas Constantinos , Chatzakou Despoina , Leontiadis Ilias , Blackburn Jeremy , Stringhini Gianluca , Vakali Athena , Sirivianos Michael , Kourtellis Nicolas
Published: November 2022

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.
Automatic Story Evolution Wikification from Social Data
Authors: Alonso Omar , Kandylas Vasileios , Tremblay Serge
Published: November 2022

We present the generation of a new and dynamic data asset that captures the evolution of a story from different perspectives. In contrast to news articles that are ranked by relevance and freshness in a search engine or a static Wikipedia article that provides an overview of the event or topic, our solution consists of the automatic construction of a wiki-like document that highlights the salient items of a topic as it evolves over time, with related pivots that allow the user to explore related stories. We demonstrate the effectiveness of our approach by processing a dataset comprising millions of English language tweets generated over a one year period.
Predicting News Coverage of Scientific Articles
Authors: MacLaughlin Ansel , Wihbey John , Smith David
Published: November 2022

Journalists act as gatekeepers to the scientific world, controlling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically receive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets using metadata and text data from a leading tracker of scientific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, using the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects.
Using Longitudinal Social Media Analysis to Understand the Effects of Early College Alcohol Use
Authors: Kiciman Emre , Counts Scott , Gasser Melissa
Published: November 2022

While college completion is predictive of individual career happiness and economic achievement, many factors, such as excessive alcohol usage, jeopardize college success. In this paper, we propose a method for analyzing large-scale, longitudinal social media timelines to provide fine-grained visibility into how the behaviors and trajectories of alcohol-mentioning students differ from their peers. Using propensity score stratification to reduce bias from confounding factors, we analyze the Twitter data of 63k college students over 5 years to study the effect of early alcohol usage on topics linked to college success. We find multi-year effects, including lower mentions of study habits, increased mentions of potentially risky behaviors, and decreases in mentions of positive emotions. We conclude with a discussion of social media data's role in the study of the risky behaviors of college students and other individual behaviors with long-term effects.
Latent Structure in Collaboration: The Case of Reddit r/place
Authors: Rappaz Jérémie , Catasta Michele , West Robert , Aberer Karl
Published: November 2022

Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q\&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r/place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r/place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.
Understanding Web Archiving Services and Their (Mis)Use on Social Media
Authors: Zannettou Savvas , Blackburn Jeremy , De Cristofaro Emiliano , Sirivianos Michael , Stringhini Gianluca
Published: November 2022

Web archiving services play an increasingly important role in today's information ecosystem, by ensuring the continuing availability of information, or by deliberately caching content that might get deleted or removed.Among these, the Wayback Machine has been proactively archiving, since 2001, versions of a large number of Web pages, while newer services like archive.is allow users to create on-demand snapshots of specific Web pages, which serve as time capsules that can be shared across the Web. In this paper, we present a large-scale analysis of Web archiving services and their use on social media, shedding light on the actors involved in this ecosystem, the content that gets archived, and how it is shared. We crawl and study: 1) 21M URLs from archive.is, spanning almost two years; and 2) 356K archive.is plus 391K Wayback Machine URLs that were shared on four social networks: Reddit, Twitter, Gab, and 4chan's Politically Incorrect board (/pol/) over 14 months. We observe that news and social media posts are the most common types of content archived, likely due to their perceived ephemeral and/or controversial nature. Moreover, URLs of archiving services are extensively shared on "fringe" communities within Reddit and 4chan to preserve possibly contentious content. Lastly, we find evidence of moderators nudging or even forcing users to use archives, instead of direct links, for news sources with opposing ideologies, potentially depriving them of ad revenue.
Forecasting the Presence and Intensity of Hostility on Instagram Using Linguistic and Social Features
Authors: Liu Ping , Guberman Joshua , Hemphill Libby , Culotta Aron
Published: November 2022

Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2).
Do Diffusion Protocols Govern Cascade Growth?
Authors: Cheng Justin , Kleinberg Jon , Leskovec Jure , Liben-Nowell David , State Bogdan , Subbian Karthik , Adamic Lada
Published: November 2022

Large cascades can develop in online social networks as people share information with one another. Though simple reshare cascades have been studied extensively, the full range of cascading behaviors on social media is much more diverse. Here we study how diffusion protocols, or the social exchanges that enable information transmission, affect cascade growth, analogous to the way communication protocols define how information is transmitted from one point to another. Studying 98 of the largest information cascades on Facebook, we find a wide range of diffusion protocols - from cascading reshares of images, which use a simple protocol of tapping a single button for propagation, to the ALS Ice Bucket Challenge, whose diffusion protocol involved individuals creating and posting a video, and then nominating specific others to do the same. We find recurring classes of diffusion protocols, and identify two key counterbalancing factors in the construction of these protocols, with implications for a cascade's growth: the effort required to participate in the cascade, and the social cost of staying on the sidelines. Protocols requiring greater individual effort slow down a cascade's propagation, while those imposing a greater social cost of not participating increase the cascade's adoption likelihood. The predictability of transmission also varies with protocol. But regardless of mechanism, the cascades in our analysis all have a similar reproduction number (≈1.8), meaning that lower rates of exposure can be offset with higher per-exposure rates of adoption. Last, we show how a cascade's structure can not only differentiate these protocols, but also be modeled through branching processes. Together, these findings provide a framework for understanding how a wide variety of information cascades can achieve substantial adoption across a network.
Mobilizing the Trump Train: Understanding Collective Action in a Political Trolling Community
Authors: Flores-Saviaga Claudia , Keegan Brian , Savage Saiph
Published: November 2022

Political trolls initiate online discord not only for the lulz (laughs), but also for ideological reasons, such as promoting their desired political candidates. Political troll groups recently gained spotlight because they were considered central in helping Donald Trump win the 2016 US presidential election, which involved difficult mass mobilizations. Political trolls face unique challenges as they must build their own communities while simultaneously disrupting others. However, little is known about how political trolls mobilize sufficient participation to suddenly become problems for others. We performed a quantitative longitudinal analysis of more than 16 million comments from one of the most popular and disruptive political trolling communities, the subreddit /r/The Donald (T_D). We use T_D as a lens to understand participation and collective action within these deviant spaces. In specific, we first study the characteristics of the most active participants to uncover what might drive their sustained participation. Next, we investigate how these active individuals mobilize their community to action. Through our analysis we uncover that the most active employed distinct discursive strategies to mobilize participation, and deployed technical tools like bots to create a shared identity and sustain engagement. We conclude by providing data-backed design implications for designers of civic media.
Characterizing Audience Engagement and Assessing Its Impact on Social Media Disclosures of Mental Illnesses
Authors: Ernala Sindhu Kiranmai , Labetoulle Tristan , Bane Fred , Birnbaum Michael L. , Rizvi Asra F. , Kane John M. , De Choudhury Munmun
Published: November 2022

Self-disclosures of mental illnesses have been identified to yield coping and therapeutic benefits. An important construct in the self-disclosure process is the audience with whom the individual interacts and shares their experiences. Mental illness self-disclosures are increasingly happening online. However, unlike online support communities where the audience comprises sympathetic peers with similar experiences, what the discloser gains from an ‘invisible’ audience on a general purpose, public social media platform is less understood. Focusing on a highly stigmatized mental illness, schizophrenia, this paper provides the first investigation characterizing the audience of disclosures of this condition on Twitter and how the audience’s engagement impacts future disclosures. Our results are based on a rich year-long temporal analysis of the data of nearly 400 disclosers and their nearly 400 thousand audiences. First, characterizing and modeling the audience engagement temporally, we find evidence of reciprocity in the disclosure process between the discloser and their audience. Then, situating our work in the Social Penetration Theory and operationalizing the disclosure process via a measure of intimacy, an auto-regressive time series model indicates that the patterns of audience engagement and content can forecast changes in the intimacy of disclosures. We discuss the implications for building socially engaging, supportive online spaces for stigmatized mental illness disclosures.
Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media
Authors: Salminen Joni , Almerekhi Hind , Milenković Milica , Jung Soon-gyo , An Jisun , Kwak Haewoon , Jansen Bernard
Published: November 2022

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.
The Impact of Recipe Features, Social Cues and Demographics on Estimating the Healthiness of Online Recipes
Authors: Rokicki Markus , Trattner Christoph , Herder Eelco
Published: November 2022

Apart from taste, people increasingly consider nutritional facts when choosing a recipe or a meal. Researchers and companies alike aim to support health-conscious choices by providing estimates of a meal's calories and levels of sugar, fat, protein and salt. Features that are typically considered for these automatic estimates include a recipe's title, ingredients and cooking directions, as well as photographic material. Little is known based on which features users estimate the healthiness of online recipes themselves. Making use of data derived from a large online food community, and data collected via crowdsourcing, we compare the performance of algorithmic nutritional estimation with the performance of human-provided estimates, and analyze the most influential features used by humans and machines. Our results indicate that simple models already outperform human raters. Basic features such as title, ingredients and cooking directions were more informative than pictures of a recipe or user comments. For human estimates, we observed effects due to age and gender, but not due to dietary preferences or cooking habits. Our quantitative and qualitative results provide guidance for the development and evaluation of methods for nutrition estimation, and give insight in which features are most useful for nudging people into making healthier diet choices.
Quantifying the Impact of Cognitive Biases in Question-Answering Systems
Authors: Burghardt Keith , Hogg Tad , Lerman Kristina
Published: November 2022

Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases. We investigate some of these biases in an experimental model of a question-answering system. We observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.
Joint Modeling of Text and Networks for Cascade Prediction
Authors: Li Cheng , Guo Xiaoxiao , Mei Qiaozhu
Published: November 2022

A critical research problem about information cascades, which is a central topic of social network analysis, is to predict the potential influence or the future growth of cascades. Recent developments of deep learning have provided promising alternatives, which no longer rely on heavy feature engineering efforts and instead learn the representation of cascade graphs in an end-to-end manner. In reality, however, the influence of a cascade not only depends on the cascade graph and the global network structure, but also largely relies on the content of the cascade and the preferences of users. In this work, we extend the deep learning approaches to cascade prediction by jointly modeling the content and the structure of cascades. We find that text information provides a valuable addition for the learning of cascade graphs, especially when some users (nodes) have rarely participated in the past cascades. To this end, a gating mechanism is introduced to dynamically fuse the structural and textual representations of nodes based on their respective properties. Attentions are employed to incorporate the text information associated with both cascade items and nodes. Empirical experiments demonstrate that incorporating text information brings a significant improvement to cascade prediction, and that the proposed model outperforms alternative ways to combine text and networks.
Graph Based Semi-Supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
Authors: Alam Firoj , Joty Shafiq , Imran Muhammad
Published: November 2022

During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situ- ational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised tech- nique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real- world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.
"Is This an STD? Please Help!": Online Information Seeking for Sexually Transmitted Diseases on Reddit
Authors: Nobles Alicia , Dreisbach Caitlin , Keim-Malpass Jessica , Barnes Laura
Published: November 2022

Increasing incidence of sexually transmitted diseases (STDs) has prompted the public health and technology communities to innovate new measures to understand how individuals use Internet resources to attain relevant information, particularly for sensitive or stigmatized conditions. The purpose of this study is to examine recent health information seeking and needs of the r/STD community, a subreddit focused exclusively on STDs. We found that the majority of posts crowdsource information about intermediate, non-reportable STDs such as human papillomavirus (HPV). Crowdsourced information in this community focused on symptoms, treatment, as well as the social and emotional aspects of sexual health such as fear of misdiagnosis. From our analysis, it is clear that online communities focused on discussion of health symptoms have the ripe potential to influence information-seeking behavior and consumer action.
Mining the Relationship between Emoji Usage Patterns and Personality
Authors: Li Weijian , Chen Yuxiao , Hu Tianran , Luo Jiebo
Published: November 2022

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.
Self-Representation on Twitter Using Emoji Skin Color Modifiers
Authors: Robertson Alexander , Magdy Walid , Goldwater Sharon
Published: November 2022

Since 2015, it has been possible to modify certain emoji with a skin tone. The five different skin tones were introduced with the aim of representing more human diversity, but some commentators feared they might be used as a way to negatively represent other users/groups. This paper presents a quantitative analysis of the use of skin tone modifiers on emoji on Twitter, showing that users with darker-skinned profile photos employ them more often than users with lighter-skinned profile photos, and the vast majority of skin tone usage matches the color of a user’s profile photo—i.e., tones represent the self, rather than the other. In the few cases where users do use opposite-toned emoji, we find no evidence of negative racial sentiment. Thus, the introduction of skin tones seems to have met the goal of better representing human diversity.
Improving Tourism Prediction Models Using Climate and Social Media Data: A Fine-Grained Approach
Authors: Khatibi Amir , Belem Fabiano , Silva Ana , Shasha Dennis , Almeida Jussara , Goncalves Marcos
Published: November 2022

Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, countries and cities invest a huge amount of money in planning and preparation in order to welcome (and profit from) tourists. An accurate prediction of the number of visits in the following days or months could help both the economy and tourists. Prior studies in this domain explore forecasting for a whole country rather than for fine-grained areas within a country (e.g., specific touristic attractions). In this work, we suggest that accessible data from online social networks and travel websites, in addition to climate data, can be used to support the inference of visitation count for many touristic attractions. To test our hypothesis we analyze visitation, climate and social media data in more than 70 National Parks in U.S during the last 3 years. The experimental results reveal a high correlation between social media data and tourism demands; in fact, in over 80\% of the parks, social media reviews and visitation counts are correlated by more than 50\%. Moreover, we assess the effectiveness of employing various prediction techniques, finding that even a simple linear regression model, when fed with social media and climate data as input features, can attain a prediction accuracy of over 80\% while a more robust algorithm, such as Support Vector Regression, reaches up to 94\% accuracy.
How Constraints Affect Content: The Case of Twitter’s Switch from 140 to 280 Characters
Authors: Gligorić Kristina , Anderson Ashton , West Robert
Published: November 2022

It is often said that constraints affect creative production, both in terms of form and quality. Online social media platforms frequently impose constraints on the content that users can produce, limiting the range of possible contributions. Do these restrictions tend to push creators towards producing more or less successful content? How do creators adapt their contributions to fit the limits imposed by social media platforms? To answer these questions, we conduct an observational study of a recent event: on November 7, 2017, Twitter changed the maximum allowable length of a tweet from 140 to 280 characters, thereby significantly altering its signature constraint. In the first study of this switch, we compare tweets with nearly or exactly 140 characters before the change to tweets of the same length posted after the change. This setup enables us to characterize how users alter their tweets to fit the constraint and how this affects their tweets' success. We find that in response to a length constraint, users write more tersely, use more abbreviations and contracted forms, and use fewer definite articles. Also, although in general tweet success increases with length, we find initial evidence that tweets made to fit the 140-character constraint tend to be more successful than similar-length tweets written when the constraint was removed, suggesting that the length constraint improved tweet quality.
A Large-Scale Study of ISIS Social Media Strategy: Community Size, Collective Influence, and Behavioral Impact
Authors: Alfifi Majid , Kaghazgaran Parisa , Caverlee James , Morstatter Fred
Published: November 2022

The Islamic State of Iraq and Syria (ISIS) has received a tremendous amount of media coverage in the past few years for their successful use of social media to spread their message and to recruit new members. In this work, we leverage access to the full Twitter Firehose to perform a largescale observational study of one year of ISIS social activity. We quantify the size of ISIS presence on Twitter, the potential amount of support it received, and its collective influence over time. We find that ISIS was able to gain a relatively limited portion from the total influence mass on Twitter and that this influence diminished over time. In addition, ISIS showed a tendency towards attracting interactions from other similar pro-ISIS accounts, while inviting only a limited anti-ISIS sentiment. We find that 75% of the interactions ISIS received on Twitter in 2015 actually came from eventually suspended accounts and that only about 8% of the interactions they received were anti-ISIS. In addition, we have created a unique dataset of 17 million ISIS-related tweets posted in 2015 which we make available for research purposes upon request.