bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring the Efficiency of Charitable Giving with Content Analysis and Crowdsourcing
Authors: Budak Ceren , Rao Justin
Published: November 2022

In the U.S., individuals give more than 200 billion dollars to over 50 thousand charities each year, yet how people make these choices is not well understood. In this study, we use data from CharityNavigator.org and web browsing data from Bing toolbar to understand charitable giving choices. Our main goal is to use data on charities' overhead expenses to better understand efficiency in the charity marketplace. A preliminary analysis indicates that the average donor is "wasting" more than 15% of their contribution by opting for poorly run organizations as opposed to higher rated charities in the same Charity Navigator categorical group. However, charities within these groups may not represent good substitutes for each other. We use text analysis to identify substitutes for charities based on their stated missions and validate these substitutes with crowd-sourced labels. Using these similarity scores, we simulate market outcomes using web browsing and revenue data. With more realistic similarity requirements, the estimated loss drops by 75%—much of what looked like inefficient giving can be explained by crowd-validated similarity requirements that are not fulfilled by most charities within the same category. A choice experiment helps us further investigate the extent to which a recommendation system could impact the market. The results indicate that money could be redirected away from the long-tail of inefficient organizations. If widely adopted, the savings would be in the billions of dollars, highlighting the role the web could have in shaping this important market.
Investigating the Observability of Complex Contagion in Empirical Social Networks
Authors: Fink Clay , Schmidt Aurora , Barash Vladimir , Kelly John , Cameron Christopher , Macy Michael
Published: November 2022

Social contagion is the mechanism by which ideas and behaviors spread across human social networks. Simple contagion models approximate the likelihood of adoption as constant with each exposure to an "infected" network neighbor. However, social theory postulates that when adopting an idea or behavior carries personal or social risk, an individual's adoption likelihood also depends on the number of distinct neighbors who have adopted. Such complex contagions are thought to govern the spread of social movements and other important social phenomena. Online sites, such as Twitter, expose social interactions at a large scale and provide an opportunity to observe the spread of social contagions "in the wild." Much of the effort in searching for complex phenomena in real world contagions focuses on measuring user adoption thresholds. In this work, we show an alternative method for fitting probabilistic complex contagion models to empirical data that avoids measuring thresholds directly, and our results indicate bias in observed thresholds under both complex and simple models. We also show 1) that probabilistic models of simple and complex contagion are distinguishable when applied to an empirical social network with random user activity; and 2) the predictive power of these probabilistic adoption models against observed adoptions of actual hashtags used on Twitter. We use a set of tweets collected from Nigeria in 2014, focusing on 20 popular hashtags, using the follow graphs of the users adopting the tags during their initial peaks of activity.
Discovering Response-Eliciting Factors in Social Question Answering : A Reddit Inspired Study
Authors: Danish  , Dahiya Yogesh , Talukdar Partha
Published: November 2022

Questions form an integral part of our everyday communication, both offline and online. Getting responses to our questions from others is fundamental to satisfying our information need and in extending our knowledge boundaries. A question may be represented using various factors such as social, syntactic, semantic, etc. We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question. We perform a thorough empirical study to measure effects of these factors using a novel question and answer dataset from the website Reddit.com. We also use a sparse non-negative matrix factorization technique to automatically induce interpretable semantic factors from the question dataset. Such interpretable factor-based analysis overcomes limitations faced by prior related research. We also document various patterns on response prediction we observe during our analysis. For instance, we found that preference-probing questions are rarely answered by actors.
“Blissfully Happy” or “Ready toFight”: Varying Interpretations of Emoji
Authors: Miller Hannah , Thebault-Spieker Jacob , Chang Shuo , Johnson Isaac , Terveen Loren , Hecht Brent
Published: November 2022

Emoji are commonly used in modern text communication. However, as graphics with nuanced details, emoji may be open to interpretation. Emoji also render differently on different viewing platforms (e.g., Apple’s iPhone vs. Google’s Nexus phone), potentially leading to communication errors. We explore whether emoji renderings or differences across platforms give rise to diverse interpretations of emoji. Through an online survey, we solicit people’s interpretations of a sample of the most popular emoji characters, each rendered for multiple platforms. Both in terms of sentiment and semantics, we analyze the variance in interpretation of the emoji, quantifying which emoji are most (and least) likely to be misinterpreted. In cases in which participants rated the same emoji rendering, they disagreed on whether the sentiment was positive, neutral, or negative 25% of the time. When considering renderings across platforms, these disagreements only increase. Overall, we find significant potential for miscommunication, both for individual emoji renderings and for different emoji renderings across platforms.
Hyphe, a Curation-Oriented Approach to Web Crawling for the Social Sciences
Authors: Jacomy Mathieu , Girard Paul , Ooghe-Tabanou Benjamin , Venturini Tommaso
Published: November 2022

The web is a field of investigation for social sciences, and platform-based studies have long proven their relevance. However the generic web is rarely studied in itself though it contains crucial aspects of the embodiment of social actors: personal blogs, institutional websites, hobby-specific media… We realized that some sociologists see existing web crawlers as “black boxes” unsuitable for research though they are willing to study the broad web. In this paper we present Hyphe, a crawler developed with and for social scientists, with an innovative “curation-oriented” approach. We expose the problems of using web-mining techniques in social science research and how to overcome those by specific features such as step-by-step corpus building and a memory structure allowing researchers to redefine dynamically the granularity of their “web entities”.
The Road to Popularity: The Dilution of Growing Audience on Twitter
Authors: Grabowicz Przemyslaw , Babaei Mahmoudreza , Kulshrestha Juhi , Weber Ingmar
Published: November 2022

On social media platforms, like Twitter, users are often interested in gaining more influence and popularity by growing their set of followers, aka their audience. Several studies have described the properties of users on Twitter based on static snapshots of their follower network. Other studies have analyzed the general process of link formation. Here, rather than investigating the dynamics of this process itself, we study how the characteristics of the audience and follower links change as the audience of a user grows in size on the road to user's popularity. To begin with, we find that the early followers tend to be more elite users than the late followers, i.e., they are more likely to have verified and expert accounts. Moreover, the early followers are significantly more similar to the person that they follow than the late followers. Namely, they are more likely to share time zone, language, and topics of interests with the followed user. To some extent, these phenomena are related with the growth of Twitter itself, wherein the early followers tend to be the early adopters of Twitter, while the late followers are late adopters. We isolate, however, the effect of the growth of audiences consisting of followers from the growth of Twitter's user base itself. Finally, we measure the engagement of such audiences with the content of the followed user, by measuring the probability that an early or late follower becomes a retweeter.
Generating Neighborhood Guides from Social Media
Authors: Tasse Dan , Chou Jennifer , Hong Jason
Published: November 2022

What are the neighborhoods in this city like? This is a question that movers and travelers ask all the time, and despite the information sources on the internet, there are few neighborhood guides to help people answer it. We describe formative research, including the Twitter Neighborhood TF-IDF map and an interview-based study of 17 recent movers and travelers. Based on this research, we then describe plans for construction of neighborhood guides based on social media posts in each neighborhood and publicly available data.
ENRICH: A Query Rewriting Service Powered by Wikipedia Graph Structure
Authors: Guisado-Gámez Joan , Tamayo-Domènech David , Urmeneta Jordi , Larriba-Pey Josep Lluís
Published: November 2022

The search for relevant information in websites can be very frustrating for users who, unintentionally, use too general or inappropriate keywords to express their requests. To overcome this situation, query rewriting techniques aim at transforming the users requests to better describe the real intent of the users. However, to the best of our knowledge, current search tools either are too generic or require resources not available for everyone such as query log processors, natural language engines, etc. To supply this need, we present ENRICH, which is a query rewriting cloud service that is automatically tailored to each website and it is powered by an available, accessible and open resource: Wikipedia.
Graph-Based Breaking News Detection on Wikipedia
Authors: Freire Ana , Manca Matteo , Saez-Trumper Diego , Laniado David , Bordino Ilaria , Gullo Francesco , Kaltenbrunner Andreas
Published: November 2022

Event detection in social media usually exploits information from social-networking platforms, such as Twitter or Facebook. However, previous research has suggested that Wikipedia constitutes a valuable source of information for the task of detecting breaking news. In this work we adapt a graph-based algorithm to the Wikipedia context, and compare it to the state-of-the-art Wikipedia real-time monitoring method. The main idea behind the proposed method is to extract breaking news by looking at unusual activity in the Wikipedia edit stream. We assess the performance of the two competing algorithms by measuring the percentage of true events correctly identified. Results show that the proposed graph-based method achieves better accuracy and coverage.
Semi-Supervised Automatic Generation of Wikipedia Articles for Named Entities
Authors: Pochampally Yashaswi , Karlapalem Kamalakar , Yarrabelly Navya
Published: November 2022

We investigate the automatic generation of Wikipedia articles as an alternative to its manual creation. We propose a framework for creating a Wikipedia article for a named entity which not only looks similar to other Wikipedia articles in its category but also aggregates the diverse aspects related to that named entity from the Web. In particular, a semi-supervised method is used for determining the headings and identifying the content for each heading in the Wikipedia article generated. Evaluations show that articles created by our system for categories like actors are more reliable and informative compared to those generated by previous approaches of Wikipedia article automation.
What Questions Do Journalists Ask on Twitter?
Authors: Hasanain Maram , Bagdouri Mossaab , Elsayed Tamer , Oard Douglas
Published: November 2022

Social media platforms are a major source of information for both the general public and for journalists. Journalists use Twitter and other social media services to gather story ideas, to find eyewitnesses, and for a wide range of other purposes. One way in which journalists use Twitter is to ask questions. This paper reports on an empirical investigation of questions asked by Arab journalists on Twitter. The analysis begins with the development of an ontology of question types, proceeds to human annotation of training and test data, and concludes by reporting the level of accuracy that can be achieved with automated classification techniques. The results show good classifier effectiveness for high prevalence question types, but that obtaining sufficient training data for lower prevalence question types can be challenging.
Using Twitter to Understand Public Interest in Climate Change: The Case of Qatar
Authors: Abbar Sofiane , Zanouda Tahar , Berti-Equille Laure , Borge-Holthoefer Javier
Published: November 2022

Climate change has received an extensive attention from public opinion in the last couple of years, after being considered for decades as an exclusive scientific debate. Governments and world-wide organizations such as the United Nations are working more than ever on raising and maintaining public awareness toward this global issue. In the present study, we examine and analyze Climate Change conversations in Qatar's Twittersphere, and sense public awareness towards this global and shared problem in general, and its various related topics in particular. Such topics include but are not limited to politics, economy, disasters, energy and sandstorms. To address this concern, we collect and analyze a large dataset of 109 million tweets posted by 98K distinct users living in Qatar -- one of the largest emitters of CO2 worldwide. We use a taxonomy of climate change topics created as part of the United Nations Pulse project to capture the climate change discourse in more than 36K tweets. We also examine which topics people refer to when they discuss climate change, and perform different analysis to understand the temporal dynamics of public interest toward these topics.
Reducing the Intrusiveness of Energy and Water End-Use Disaggregation via Social Media and Users Interactions
Authors: Giuliani Matteo , Mossina Jacopo
Published: November 2022

Water end-use disaggregation from smart-metered water consumption data is key to design water demand management strategies. Yet, despite large investments for smart metering networks, few water utilities are actually exploiting the potential of end-use-based consumer profiling. This is mostly due to the intrusiveness of existing disaggregation algorithms, which require intensive and time consuming human interaction for extracting appliance-specific information and estimating the end-use patterns. We propose to explore the potential for social media in facilitating the interactions between water utilities and water users in order to collect consumption diaries. The evaluation of the value of this user-generated information is performed by using the collected diaries as input for a disaggregation algorithm, which is shown to attain acceptable performance in reproducing the end-use patterns.
Programming Languages in GitHub: A Visualization in Hyperbolic Plane
Authors: Celińska Dorota , Kopczyński Eryk
Published: November 2022

GitHub is nowadays the largest software repository hosting service that incorporates social media functionalities to writing and assessing source code. We visualize the weighted network of programming languages used by the registered users of GitHub as of December 2016. There is an edge in the graph between language A and B if and only if A and B are both used by the same user in any of her repositories. The weight depends on the number of times such edge appears. Our visualization utilizes hyperbolic geometry, which is intrinsic to networks based on similarity and popularity. RogueViz, a novel tool, based on the Open Source game HyperRogue, is used to map the network and navigate the hyperbolic graph.
Online Popularity Under Promotion: Viral Potential, Forecasting, and the Economics of Time
Authors: Rizoiu Marian-Andrei , Xie Lexing Xie
Published: November 2022

Modeling the popularity dynamics of an online item is an important open problem in computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.
When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers
Authors: Ye Teng , You Sangseok , Robert Jr. Lionel
Published: November 2022

This research adds to the rich discussion on whether increases in payment to crowdworkers lead to increases in performance quality by introducing the concept of perceived fairness in pay (PFP). PFP refers to the belief that one is fairly compensated for their work. We examine whether PFP mediates the impact of payment amount on the performance quality of crowdworkers. We conducted a field experiment with 152 crowdworkers performing a button-clicking (BC) task and an instructional manipulation check (IMC) task. PFP mediated the impact of payment amount on performance quality in the BC task but not in the IMC task. PFP also mediated the impact of payment amount on satisfaction and task time. Results suggest that PFP can help us better understand the relationship between payment and performance quality in crowdsourcing.
Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations
Authors: Chakraborty Abhijnan , Messias Johnnatan , Benevenuto Fabricio , Ghosh Saptarshi , Ganguly Niloy , Gummadi Krishna
Published: November 2022

Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (for example, Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service Who-Makes-Trends at twitter-app.mpi-sws.org/who-makes-trends.
Wearing Many (Social) Hats: How Different Are Your Different Social Network Personae?
Authors: Zhong Changtao , Chang Hau-wen , Karamshuk Dmytro , Lee Dongwon , Sastry Nishanth
Published: November 2022

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user’s profile image (0.657–0.829 AUC) or self-description (0.608–0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.
Twitter911: A Cautionary Tale
Authors: Zaman Anis , Hossain Nabil , Kautz Henry
Published: November 2022

Researchers have argued that social media, and in particular, Twitter, can be searched to improve “situational awareness” in emergency situations; that is, to provide objective, actionable real-time information to first-responders. Prior studies have examined cases of very rare, catastrophic emergencies that took place over many days, such as the aftermath of Hurricane Sandy. We asked instead if Twitter could pro- vide useful information for first-responders on a more regular basis, by conducting an exhaustive analysis of tweets and fire department data for medium-sized county (population 1 million), and for two larger-scale single-day emergencies in New York City. Our results are resoundingly negative: useful tweets were extraordinarily rare or nonexistence. This study provides a cautionary note as to the potential of Twitter and similar platforms for emergency situational awareness.
Sparse Overlap Cross-Platform Recommendation Via Adaptive Similarity Structure Regularization
Authors: Lu Hanqing , Chen Chaochao , Jiang Qinyue
Published: November 2022

People often use multiple platforms to fulfill their different information needs, which has opened opportunities for research on the cross-platform recommendation. Existing cross-platform recommendation works either assume no overlapping users on different platforms or require enough overlapping users to reach a good performance. None of them pays attention to the sparse overlap problem, that is, the number of observed overlapping users of different platforms is very small. In this paper, we propose a cross-platform recommendation framework termed Adaptive Similarity Structure Regularization Through Connector (AdaSTC), which adaptively learns the user similarity structure on different platforms and further uses it to regularize the modeling process of user preference. Experiments conducted on two real-world datasets demonstrate that AdaSTC significantly outperforms the state-of-the-art methods in the sparse overlap situation.
A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles
Authors: Ma Xiao , Neeraj Trishala , Naaman Mor
Published: November 2022

We developed a novel computational framework to predict the perceived trustworthiness of host profile texts in the context of online lodging marketplaces. To achieve this goal, we developed a dataset of 4,180 Airbnb host profiles annotated with perceived trustworthiness. To the best of our knowledge, the dataset along with our models allow for the first computational evaluation of perceived trustworthiness of textual profiles, which are ubiquitous in online peer-to-peer marketplaces. We provide insights into the linguistic factors that contribute to higher and lower perceived trustworthiness for profiles of different lengths.
Recognizing Pathogenic Empathy in Social Media
Authors: Abdul-Mageed Muhammad , Buffone Anneke , Peng Hao , Eichstaedt Johannes , Ungar Lyle
Published: November 2022

Empathy is an integral part of human social life, as people care about and for others who experience adversity. However, a specific “pathogenic” form of empathy, marked by automatic contagion of negative emotions, can lead to stress and burnout. This is particularly detrimental for individuals in caregiving professions who experience empathic states more frequently, because it can result in illness and high costs for health systems. Automatically recognizing pathogenic empathy from text is potentially valuable to identify at-risk individuals and monitor burnout risk in caregiving populations. We build a model to predict this type of empathy from social media language on a data set we collected of users’ Facebook posts and their answers to a new questionnaire measuring empathy. We obtain promising results in identifying individuals' empathetic states from their social media (Pearson r = 0.252, p <0.003).
Early Identification of Personalized Trending Topics in Microblogging
Authors: Wu Liang , Hu Xia , Liu Huan
Published: November 2022

Social media has become a primary platform for the spread of information. Trending topics, which are breaking news and immediately popular stories, have become an attractive data source facilitating the spread of emerging issues. Motivated by the diverse trending topics covering from sports to politics, it is essential to help users find personalized trending topics. Since a topic in social media may start trending and get obsoleted quickly, the personalization would be more valuable to a user if the trending topic can be recommended before it is outdated. In order to identify personalized trending topics at an early stage, we propose to identify and exploit the auxiliary information. In particular, through collectively modeling content of similar users with social network information, we identify additional past contents that can enrich the training data of trending topics and users. The key insight is that though most posts of a user may be irrelevant, a few key posts can be signals revealing interests towards a particular topic. Experiments on real-world data demonstrate that our proposed approach effectively personalizes trending topics when they just start trending.
A First Look at User Switching Behaviors Over Multiple Video Content Providers
Authors: Yan Huan , Lin Tzu-Heng , Wang Gang , Li Yong , Zheng Haitao , Jin Depeng , Zhao Ben
Published: November 2022

Watching videos from multiple content providers (CP) has become prevalent. For individual CPs, understanding user video consumption patterns among CPs is critical for improving on-site user experience and CP's opportunity of success. In this paper, based on a two-month dataset recording 9 million users' 269 million video viewing requests over 6 most popular video CPs in China, we provide a first look at users' video consumption and switching behaviors across different CPs. We find that rarely do active video watchers stick to one single CP. When users switch from one CP to another, they are more likely to switch to larger CPs with more videos.
From Camera to Deathbed: Understanding Dangerous Selfies on Social Media
Authors: Lamba Hemank , Bharadhwaj Varun , Vachher Mayank , Agarwal Divyansh , Arora Megha , Sachdeva Niharika , Kumaraguru Ponnurangam
Published: November 2022

Selfie culture has emerged as a ubiquitous instrument for self portrayal in recent years. To portray themselves differently and attractive to others, individuals may risk their life by clicking selfies in dangerous situations. Consequently, selfies have claimed 137 lives around the world since March 2014 until December 2016. In this work, we perform a comprehensiv analysis of the reported selfie-casualties and note various reasons behind these deaths. We perform an in-depth analysis of such selfies posted on social media to identify dangerous selfies and explore a series of statistical models to predict dangerous posts. We find that our multimodal classifier using combination of text-based, image-based and location-based features performs the best in spotting dangerous selfies. Our classifier is trained on 6K annotated selfies collected on Twitter and gives 82% accuracy for identifying whether a selfie posted on Twitter is dangerous or not.
Ranking with Social Cues: Integrating Online Review Scores and Popularity Information
Authors: Pipergias Analytis Pantelis , Delfino Alexia , Kämmer Juliane , Moussaid Mehdi , Joachims Thorsten
Published: November 2022

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity — that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.
Nasty, Brutish, and Short: What Makes Election News Popular on Twitter?
Authors: Chou Sophie , Roy Deb
Published: November 2022

During the 2016 U.S. presidential elections, Twitter served as an important platform for the spread of news articles, which have significant influence on public opinion. Yet the sharing of stories is often based on innate emotional triggers, seldom rational. In our research, we seek to examine whether the emotional vocabulary of political news stories can lead to their popularity. To explore these questions, we construct a corpus of 2,650 articles from 12 different news publications over 5 months, connected with the 123,113 tweets by 20,964 Twitter users that share them. Using the Harvard Inquirer lexicons, we automatically code stories for emotionality and positivity. We then run regressions between the independent variables of story length, emotionality, and positivity and the dependent variable of number of shares across 7 different political divisions of Twitter users, as well as the collective dataset. On the whole, we find Twitter users to favor stories that are Hobbesian in nature: nasty (negative in positivity), brutish (high in emotionality), and short (low in word count). However, differences emerge when considering different levels of political engagement among users.
Urban Maps of Social Activity
Authors: Whiting Stewart , Alonso Omar , Kandylas Vasileios , Tremblay Serge
Published: November 2022

Recreational queries from users looking for ideas of what to do or see and where to go are very common in desktop, mobile and, increasingly, contextual search scenarios. Such queries typically contain what, where, when components as the user seeks future activities, whether in real-time or future trip planning. Often, the user will have additional constraints and requirements for what they are seeking, such as suitability for kids, budget, for a romantic date, etc. Currently, simple recreational queries (e.g. ``restaurants in Mountain View’'' are served by static local results, or the Points of Interest thumbnail carousel (e.g. ``things to see in San Francisco''). More complex recreational queries, such as ``romantic places to eat in San Francisco friday night'', or ``educational places to visit with kids nearby'' require the user to click on the 10 blue links to read through articles from sites such as TripAdvisor and WikiTravel to satisfy their needs. Employing location based social network data, we construct urban maps of social activity for answering recreational queries using a model based on social, geographical, and temporal information. We demonstrate the feasibility of our approach using a data set of 1B check-ins.
AbuSniff: Automatic Detection and Defenses Against Abusive Facebook Friends
Authors: Talukder Sajedul , Carbunar Bogdan
Published: November 2022

Adversaries leverage social network friend relationships to collect sensitive data from users and target them with abuse that includes fake news, cyberbullying, malware, and propaganda. Case in point, 71 out of 80 user study participants had at least 1 Facebook friend with whom they never interact, either in Facebook or in real life, or whom they believe is likely to abuse their posted photos or status updates, or post offensive, false or malicious content. We introduce AbuSniff, a system that identifies Facebook friends perceived as strangers or abusive, and protects the user by unfriending, unfollowing, or restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse. We introduce mutual Facebook activity features and show that they can train supervised learning algorithms to predict questionnaire responses. We have evaluated AbuSniff through several user studies with a total of 263 participants from 25 countries. After answering the questionnaire, participants agreed to unfollow and restrict abusers in 91.6% and 90.9% of the cases respectively, and sandbox or unfriend non-abusive strangers in 92.45% of the cases. Without answering the questionnaire, participants agreed to take the AbuSniff suggested action against friends predicted to be strangers or abusive, in 78.2% of the cases. AbuSniff increased the participant self-reported willingness to reject invitations from strangers and abusers, their awareness of friend abuse implications and their perceived protection from friend abuse.
You Are Your Metadata: Identification and Obfuscation of Social Media Users Using Metadata Information
Authors: Perez Beatrice , Musolesi Mirco , Stringhini Gianluca
Published: November 2022

Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still categorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that, through the application of a supervised learning algorithm, we are able to identify any user in a group of 10,000 with approximately 96.7% accuracy. Moreover, if we broaden the scope of our search and consider the 10 most likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms.
Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation Using Visual Analytics
Authors: Karduni Alireza , Wesslen Ryan , Santhanam Sashank , Cho Isaac , Volkova Svitlana , Arendt Dustin , Shaikh Samira , Dou Wenwen
Published: November 2022

We describe a novel study of decision-making processes around misinformation on social media. Using a custom-built visual analytic system, we presented users with news content from social media accounts from a variety of news outlets, including outlets engaged in distributing misinformation. We conducted controlled experiments to study decision-making regarding the veracity of these news outlets and tested the role of confirmation bias (the tendency to ignore contradicting information) and uncertainty of information on human decision-making processes. Our findings reveal that the presence of conflicting information, presented to users in the form of cues, impacts the ability to judge the veracity of news in systematic ways. We also find that even instructing participants to explicitly disconfirm given hypotheses does not significantly impact their decision-making regarding misinformation when compared to a control condition. Our findings have the potential to inform the design of visual analytics systems so that they may be used to mitigate the effects of cognitive biases and stymie the spread of misinformation on social media.
The Dynamics of Peer-Produced Political Information During the 2016 U.S. Presidential Campaign
Authors: Keegan Brian
Published: November 2022

Wikipedia plays a crucial role for online information seeking and its editors have a remarkable capacity to rapidly revise its content in response to current events. How did the production and consumption of political information on Wikipedia mirror the dynamics of the 2016 U.S. Presidential campaign?Drawing on systems justification theory and methods for measuring the enthusiasm gap among voters, this paper quantitatively analyzes the candidates’ biographical and related articles and their editors. Information production and consumption patterns match major events over the course of the campaign, but Trump-related articles show consistently higher levels of engagement than Clinton-related articles. Analysis of the editors’ participation and backgrounds show analogous shifts in the composition and durability of the collaborations around each candidate.
Extracting Predictive Statements with Their Scope from News Articles
Authors: Yarrabelly Navya , Karlapalem Kamalakar
Published: November 2022

We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting future-related information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.
Bumper Stickers on the Twitter Highway: Analyzing the Speed and Substance of Profile Changes
Authors: Wesslen Ryan , Nandu Sagar , Eltayeby Omar , Gallicano Tiffany , Levens Sara , Jiang Min , Shaikh Samira
Published: November 2022

We describe a novel longitudinal study of the frequency and significance of social media users' profile changes. Drawing upon two formative theories from communication and psychology: self-construal and signaling theory, we examine the likelihood that users will change their profiles and what constitutes a significant profile change. Our findings indicate that users are more likely to change their Profile Summaries and Display Names than their Locations and Screen Names (i.e. handles). Further, we used topic modeling to partition users based on their profiles to identify themes and explored how profile changes differ among these thematic groups (e.g., Trump supporters). Last, we identified the most significant word changes by users in their profiles. Our findings provide valuable baseline data for further study of Twitter profiles, including the spread of social contagion through these profiles.
Is the Sharing Economy About Sharing at All? A Linguistic Analysis of Airbnb Reviews
Authors: Quattrone Giovanni , Nicolazzo Serena , Nocera Antonino , Quercia Daniele , Capra Licia
Published: November 2022

The sharing economy manifesto emphasises sharing underused resources as a means to build stronger communities. This manifesto has however received strong critiques that claim these markets are all about access as opposed to sharing, and that consumers are after utilitarian, as opposed to social, value. Being able to assess whether an economy is about access or sharing has important implications for how companies operate, and compete, in this space. To help shed light onto this, we perform a linguistic analysis of the reviews that peers in sharing economy markets leave to one another. We take the case of Airbnb as an example, and U.S. in particular, and identify the main themes that peers discuss in their reviews. Our findings reveal that utilitarian values (e.g., properties' facilities, convenience of location, business conduct) are discussed much more frequently than social values (e.g., guest/host interactions) and that this gap substantially increases over time.
$FAKE: Evidence of Spam and Bot Activity in Stock Microblogs on Twitter
Authors: Cresci Stefano , Lillo Fabrizio , Regoli Daniele , Tardelli Serena , Tesconi Maurizio
Published: November 2022

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.
Filtered Food and Nofilter Landscapes in Online Photography:The Role of Content and Visual Effects in Photo Engagement
Authors: Bakhshi Saeideh , Kennedy Lyndon , Gilbert Eric , Shamma David A.
Published: November 2022

Millions of images are shared through social media every day. Yet, we know little about how the activities and preferences of users are dependent on the content of these images. In this paper, we seek to understand viewers engagement with photos. We design a quantitative study to expand previous research on in-app visual effects (also known as filters) through the examination of visual content identified through computer vision. This study is based on analysis of 4.9M Flickr images and is organized around three important engagement factors: likes, comments and favorites. We find that filtered photos are not equally engaging across different categories of content. Photos of food and people attract more engagement when filters are used, while photos of natural scenes and photos taken at night are more engaging when left unfiltered. In addition to contributing to the research around social media engagement and photography practices, our findings offer several design implications for mobile photo sharing platforms.
Who Should Be the Captain This Week?Leveraging Inferred Diversity-Enhanced Crowd Wisdom for a Fantasy Premier League Captain Prediction
Authors: Bhatt Shreyansh , Chen Keke , Shalin Valerie L. , Sheth Amit P. , Minnery Brandon
Published: November 2022

Participants in Fantasy Sports make a critical decision: selecting productive players for their fantasy team. The wellestablished Wisdom of Crowd effect can predict productive, rewarding players; popular, frequently selected players are potentially good choices. Previous performance data permits the identification of a subset of participants who collectively predict productive players. However, performance data may not always be available. Here we study the assembly of a small subset of the crowd a priori using another important crowd property: semantic diversity. We infer diversity from participants’ Twitter posts (tweets) that users voluntarily, and naturally provide as part of their reasoning. We propose the SmartCrowd framework to select a small, smart crowd using participants’ Twitter posts. SmartCrowd includes three steps: 1) characterize participants using their social media posts with summary word vectors, 2) cluster participants based on these vectors, and 3) sample participants from these clusters, maximizing multiple diversity measures to form final diverse crowds. We evaluated our approach to diversity characterization for the Fantasy Premier League (FPL) captain prediction problem, in which participants predict a successful weekly captain among a set of soccer players. Empirical evaluation shows that SmartCrowd generates diverse crowds outperforming random crowds, 93% of individual participants, and crowds consisting of the top 10%, 20% experts identified from previous performance data. We provide converging evidence that social media based diversity supports the sampling of smarter crowds that collectively predict productive players. These results have implications for other domains, such as economics and geopolitical forecasting, that benefit from aggregated judgments.
The Use of Deep Learning Distributed Representations in the Identification of Abusive Text
Authors: Chen Hao , McKeever Susan , Delany Sarah Jane
Published: November 2022

The selection of optimal feature representations is a critical step in the use of machine learning in text classification. Traditional features (e.g. bag of words and n-grams) have dominated for decades, but in the past five years, the use of learned distributed representations has become increasingly common. In this paper, we summarise and present a categorisation of the stateof-the-art distributed representation techniques, including word and sentence embedding models. We carry out an empirical analysis of the performance of the various feature representations using the scenario of detecting abusive comments. We compare classification accuracies across a range of off-the-shelf embedding models using 10 labelled datasets gathered from different social media platforms. Our results show that multi-task sentence embedding models perform best with consistently highest classification results in comparison to other embedding models. We hope our work can be a guideline for practitioners in selecting appropriate features in text classification task, particularly in the domain of abuse detection.