bluuebunny/crossref_metadata_2025 huggingface dataset:
Who Are Like-Minded: Mining User Interest Similarity in Online Social Networks
Authors: Yang Chunfeng , Zhou Yipeng , Chiu Dah Ming
Published: November 2022

In this paper, we mine and learn to predict how similar a pair of users’ interests towards videos are, based on demographic, social and interest information of these users. We use the video access patterns of active users as ground truth. We adopt tag-based user profiling to establish this ground truth. We then show the effectiveness of the different features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and tree-based models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation.
Learning the Relationships between Drug, Symptom, and Medical Condition Mentions in Social Media
Authors: Yates Andrew , Goharian Nazli , Frieder Ophir
Published: November 2022

We consider the general problem of learning relationships between drugs, symptoms, and medical conditions mentioned on Twitter, with the goal of estimating probability distributions to reduce the difficulties presented by social media's incomplete picture. If a user mentions taking a drug and experiencing several unexpected symptoms, for example, are the symptoms associated with that drug or is it more likely that the symptoms are associated with an unmentioned underlying condition? We describe a model for learning from and utilizing such relationships. We demonstrate that our approach identifies drugs that are similar based on their associated symptoms (or conditions), identifies conditions that are similar based on their associated symptoms, and can determine whether a symptom is caused by a medical condition or by a drug (i.e., a drug side effect).
InsightMe: Raising Awareness of Conveyed Personality in Social Media Traces
Authors: Xu Bin , Gou Liang , Xu Anbang , Cosley Dan , Mahmud Jalal
Published: November 2022

Users’ persistent social media contents like posts on Facebook Timeline are presented as an “exhibition” about the person to others, and managing these exhibitional contents for desired online image needs intentional and manual efforts. To raise awareness of and facilitate the management of conveyed personality image around past contents, we developed a prototype called InsightMe. The system employs computational psycho-linguistic analysis to help users visualize the way their past text posts might convey impressions of their personality and allowed users to modify their posts based on these analysis. We conducted a user study to evaluate the design; users overall found that such a tool raised awareness of the fact and the ways personality might be conveyed through their past content as one aspect of impression management, but that it needs design improvement to offer actionable suggestions for content modification, as well as careful thinking about impression management as one of many values people have about their digital past.
Who Will Respond to Your Requests for Instant Trouble-Shooting?
Authors: Hsu Kai-Hsiang , Li Cheng-Te , Tseng Chien-Lin
Published: November 2022

Instant mentoring services are novel social media, in which mentees can input expertise requests and wait for accepting some expert mentors who are willing to tackle the requests in an instant and one-by-one manner. While mentee's satisfaction of being mentored is determined by the matched mentor, this paper aims at analyzing and finding which mentors will respond to the given request raised by a mentee in Codementor, which is one of the popular instant mentoring services. We formulate the Mentor Willingness Ranking (MWR) problem. MWR is to understand whether a mentor is willing to tackle a request. We propose to deal with the task by generating a ranked list of mentors such that those mentors who are really willing to tackle the request are as many as possible. We develop three categories of features, Availability, Capability, and Activity, to model the willingness of a mentor dealing with the request. Results of analysis show the effectiveness of these features, and encourage develop learning-based method to accurately identify the willing mentors.
Freshman or Fresher? Quantifying the Geographic Variation of Language in Online Social Media
Authors: Kulkarni Vivek , Perozzi Bryan , Skiena Steven
Published: November 2022

In this paper we present a new computational technique to detect and analyze statistically significant geographic variation in language. While previous approaches have primarily focused on lexical variation between regions, our method identifies words that demonstrate semantic and syntactic variation as well. We extend recently developed techniques for neural language models to learn word representations which capture differing semantics across geographical regions. In order to quantify this variation and ensure robust detection of true regional differences, we formulate a null model to determine whether observed changes are statistically significant. Our method is the first such approach to explicitly account for random variation due to chance while detecting regional variation in word meaning. To validate our model, we study and analyze two different massive online data sets: millions of tweets from Twitter as well as millions of phrases contained in the Google Book Ngrams. Our analysis reveals interesting facets of language change across countries.
Expertise in Social Networks: How Do Experts Differ from Other Users?
Authors: Horne Benjamin , Nevo Dorit , Freitas Jesse , Ji Heng , Adali Sibel
Published: November 2022

Expertise location is a difficult task, with expertise often being implied and liable to change. In this paper we propose a heuristic-based approach for automated identification of expertis eon Twitter. We collect tweets from experts and non-experts in different domains and compute different types of features based on the heuristics regarding properties of the messages written and re-tweeted by the experts. We show that these heuristics provide us with interesting insights regarding how experts differ from other user groups which can help guide future studies in this areas and algorithms for expertise location.
On the Impact of Social Cost in Opinion Dynamics
Authors: Liakos Panagiotis , Papakonstantinopoulou Katia
Published: November 2022

We study the formation of opinions in a social context. It has been observed that when in a social environment people often average their opinion with their friends' opinions, so as to highlight their common beliefs. We analyze a popular social network and verify that social interaction indeed results in influence on opinions among the participants. Moreover we create instances of the network using real data and, based on the game theory framework, we experimentally show that the repeated averaging process results to Nash equilibria which are illustrative of how users really behave.
A Semi-Automatic Method for Efficient Detection of Stories on Social Media
Authors: Vosoughi Soroush , Roy Deb
Published: November 2022

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.
Analyzing the Targets of Hate in Online Social Media
Authors: Silva Leandro , Mondal Mainack , Correa Denzil , Benevenuto Fabrício , Weber Ingmar
Published: November 2022

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.
Using Organizational Social Networks to Predict Employee Engagement
Authors: Guha Shion , Muller Michael , Shami N Sadat , Masli Mikhil , Geyer Werner
Published: November 2022

Employee engagement (EE) has been shown to have important implications for the success of organizations. Most researchers have discussed employee engagement in terms of factors in a top-down, hierarchical model of the organization. However, there may also be contributing factors from an employee‟s intra-organizational social network. (IOSN). In this paper, we show that an employee‟s social network attributes can contribute to the prediction of engagement, primarily through centrality and homophily in a large, multinational company. Our research expands the range of theoretical factors that can predict employee engagement from a top-down vertical model to a mixed-factor horizontal model. We discuss how this work points toward a richer set of methods to predict engagement, as well as new ways of thinking about organizational networks.
Pinning Alone? A Study of the Role of Social Ties on Pinterest
Authors: Zhong Changtao , Kourtellis Nicolas , Sastry Nishanth
Published: November 2022

This paper seeks to answer the question of whether social ties are important on interest-driven social networks, by analysing 4-years of activities of 50,000 randomly sampled users on Pinterest, a social image discovery website. We find that a non-trivial number of users’ images are copied or repinned from strangers instead of friends, suggesting that social-based information exploration is not important. However, social interactions and social repins are critical for user retention: users interacting with friends are more likely to return Pinterest soon. These results suggest that the real role of social ties on interest-driven social networks is to enable bonding of users rather than seeking information.
Capturing Real-Time Public Space Activity Using Publicly Available Digital Traces
Authors: Cheliotis Kostas
Published: November 2022

The study of public space activity has been one of the main foci in the debate on urban space transformations for the past decades, with many researchers adding to the debate through theoretical work, as well as empirical/quantitative evidence, drawing from their direct observations of public space activity. This paper attempts to enhance this approach of urban research and public space observation, by investigating the application of remote sensing techniques in public space analysis. More specifically, it attempts to capture public space activity using publicly available digital traces, such as environmental and temporal data, as well as social media data streams. By applying bivariate and multivariate analysis techniques to these datasets, it illustrates the possibility of capturing current activity in public spaces, with some degree of confidence. Furthermore, given the ubiquitous and real-time nature of these datasets, it also becomes possible to provide continuous estimates as well as short-term predictions on current and near-future public space use. Finally, it outlines the capabilities of this approach, to be used in complementary fashion to direct observation methods mentioned above, in building high resolution models and simulations of public space activity.
State of the Union: A Data Consumer's Perspective on Wikidata and Its Properties for the Classification and Resolution of Entities
Authors: Spitz Andreas , Dixit Vaibhav , Richter Ludwig , Gertz Michael , Geiss Johanna
Published: November 2022

Wikipedia is one of the most popular sources of free data on the Internet and subject to extensive use in numerous areas of research. Wikidata on the other hand, the knowledge base behind Wikipedia, is less popular as a source of data, despite having the "data" already in its name, and despite the fact that many applications in Natural Language Processing in general and Information Extraction in particular benefit immensely from the integration of knowledge bases. In part, this imbalance is owed to the younger age of Wikidata, which launched over a decade after Wikipedia. However, this is also owed to challenges posed by the still evolving properties of Wikidata that make its content more difficult to consume for third parties than is desirable. In this article, we analzye the causes of these challenges from the viewpoint of a data consumer and discuss possible avenues of research and advancement that both the scientific and the Wikidata community can collaborate on to turn the knowledge base into the invaluable asset that it is uniquely positioned to become.
Literature, Geolocation and Wikidata
Authors: Nielsen Finn
Published: November 2022

Littar was the second-prize winning entry in an app competition. It implemented a system for visualizing places mentioned in individual literary works. Wikidata acted as the backend for the system. Here I describe the Littar system and also some of the issues I encountered while developing the system: How locations and literature can be related, what types of location-literature relations are possible within Wikidata, what limitations there are and what questions we may ask once we have enough data in Wikidata.
An Analysis of Event-Agnostic Features for Rumour Classification in Twitter
Authors: Tolosi Laura , Tagarev Andrey , Georgiev Georgi
Published: November 2022

Recently, much attention has been given to models for identifying rumors in social media. Features that are helpful for automatic inference of credibility, veracity, reliability of information have been described. The ultimate goal is to train classification models that are able to recognize future high-impact rumors as early as possible, before the event unfolds. The generalization power of the models is greatly hindered by the domain-dependent distributions of the features, an issue insufficiently discussed. Here we study a large dataset consisting of rumor and non-rumor tweets commenting on nine breakingnews stories taking place in different locations of the world. We found that the distribution of most features are specific to the event and that this bias naturally affects the performance of the model. The analysis of the domain-specific feature distributions is insightful and hints to the distinct characteristics of the underlying social network for different countries, social groups, cultures and others.
Using Social Media to Detect and Locate Wildfires
Authors: Boulton Chris , Shotton Humphrey , Williams Hywel
Published: November 2022

Methods for detecting and tracking natural hazards continue to increase in coverage, resolution and reliability. However, information on the social impacts of natural hazards is often lacking. Here we test the feasibility of using social media data (Twitter and Instagram) to detect and map an important class of natural hazard: wildfires. We analyse social media posts associated with wildfires over several time periods and compare them with wildfire occurrence data derived from satellite-based remote sensing data and on-the-ground observations. For the whole of the contiguous United States, we find significant temporal correlations between wildfire-related social media activity and wildfire occurrence, but also that there is substantial variation in the strength of this relationship at smaller spatial scales (states and counties). We then explore the utility of social media for location of wildfire events, finding good evidence to support further development of such methods. We conclude by discussing several challenges and opportunities for application of this novel data resource to provide information on impacts of natural hazards.
EmojiNet: An Open Service and API for Emoji Sense Discovery
Authors: Wijeratne Sanjaya , Balasuriya Lakshika , Sheth Amit , Doran Derek
Published: November 2022

This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web. EmojiNet is a dataset consisting of: (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition; and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji. The dataset is hosted as an open service with a REST API and is available at http://emojinet.knoesis.org/. The development of this dataset, evaluation of its quality, and its applications including emoji sense disambiguation and emoji sense similarity are discussed.
Spice Up Your Chat: The Intentions and Sentiment Effects of Using Emojis
Authors: Hu Tianran , Guo Han , Sun Hao , Nguyen Thuy-vy , Luo Jiebo
Published: November 2022

Emojis, as a new way of conveying nonverbal cues, are widely adopted in computer-mediated communications. In this paper, first from a message sender perspective, we focus on people's motives in using four types of emojis — positive, neutral, negative, and non-facial. We compare the willingness levels of using these emoji types for seven typical intentions that people usually apply nonverbal cues for in communication. The results of extensive statistical hypothesis tests not only report the popularities of the intentions, but also uncover the subtle differences between emoji types in terms of intended uses. Second, from a perspective of message recipients, we further study the sentiment effects of emojis, as well as their duplications, on verbal messages. Different from previous studies in emoji sentiment, we study the sentiments of emojis and their contexts as a whole. The experiment results indicate that the powers of conveying sentiment are different between four emoji types, and the sentiment effects of emojis vary in the contexts of different valences.
Armed Conflicts in Online News: A Multilingual Study
Authors: West Robert , Pfeffer Jürgen
Published: November 2022

Wars and conflicts have constituted major events throughout history. Despite their importance, the general public typically learns about such events only indirectly, through the lens of news media, which necessarily select and distort events before relaying them to readers. Quantifying these processes is important, as they are fundamental to how we see the world, but the task is difficult, as it requires working with large and representative datasets of unstructured news text in many languages. To address these issues, we propose a set of unsupervised methods for compiling and analyzing a multilingual corpus of millions of online news documents about armed conflicts. We then apply our methods to answer a number of research questions: First, how widely are armed conflicts covered by online news media in various languages, and how does this change as conflicts progress? Second, what role does the level of violence of a conflict play? And third, how well informed is a reader when following a limited number of online news sources? We find that coverage levels are different across conflicts, but similar across languages for a given conflict; that Middle Eastern conflicts receive more attention than African conflicts, even when controlling for the level of violence; and that for most languages and conflicts, following very few sources is enough to stay continuously informed. Finally, given the prominence of conflicts in the Middle East, we further analyze them in a detailed case study.
Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia
Authors: Zhang Ark Fangzhou , Livneh Danielle , Budak Ceren , Robert Lionel , Romero Daniel
Published: November 2022

Collaborative crowdsourcing has become a popular approach to organizing work across the globe. Being global also means being vulnerable to shocks — unforeseen events that disrupt crowds — that originate from any country. In this study, we examine changes in collaborative behavior of editors of Chinese Wikipedia that arise due to the 2005 government censorship in mainland China. Using the exogenous variation in the fraction of editors blocked across different articles due to the censorship, we examine the impact of reduction in group size, which we denote as the shock level, on three collaborative behavior measures: volume of activity, centralization, and conflict. We find that activity and conflict drop on articles that face a shock, whereas centralization increases. The impact of a shock on activity increases with shock level, whereas the impact on centralization and conflict is higher for moderate shock levels than for very small or very high shock levels. These findings provide support for threat rigidity theory — originally introduced in the organizational theory literature — in the context of large-scale collaborative crowds.
DeepCity: A Feature Learning Framework for Mining Location Check-Ins
Authors: Pang Jun , Zhang Yang
Published: November 2022

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographics and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms state-of-the-art models significantly.
A World of Difference: Divergent Word Interpretations Among People
Authors: Hu Tianran , Song Ruihua , Abtahian Maya , Ding Philip , Xie Xing , Luo Jiebo
Published: November 2022

Divergent word usages reflect differences among people. In this paper, we present a novel angle for studying word usage divergence — word interpretations. We propose an approach that quantifies semantic differences in interpretations among different groups of people. The effectiveness of our approach is validated by quantitative evaluations. Experiment results indicate that divergences in word interpretations exist. We further apply the approach to two well studied types of differences between people — gender and region. The detected words with divergent interpretations reveal the unique features of specific groups of people. For gender, we discover that certain different interests, social attitudes, and characters between males and females are reflected in their divergent interpretations of many words. For region, we find that specific interpretations of certain words reveal the geographical and cultural features of different regions.
Event Organization 101: Understanding Latent Factors of Event Popularity
Authors: Zhang Shuo , Lv Qin
Published: November 2022

The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence Network, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.
Detecting the Hate Code on Social Media
Authors: Magu Rijul , Joshi Kshitij , Luo Jiebo
Published: November 2022

Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google’s Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.
Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media
Authors: Kocabey Enes , Camurcu Mustafa , Ofli Ferda , Aytar Yusuf , Marin Javier , Torralba Antonio , Weber Ingmar
Published: November 2022

A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming'" and other forms of "sizeism'' are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.
Distinguishing the Wood from the Trees: Contrasting Collection Methods to Understand Bias in a Longitudinal Brexit Twitter Dataset
Authors: Llewellyn Clare , Cram Laura
Published: November 2022

Various methods can be used for searching or streaming Twitter data to gather a sample on a specific topic. All of these methods introduce a bias into the resulting datasets. Here we examine, and try to define, the bias that the different strategies introduce. Understanding the bias means that we can extrapolate wider meaning from the data in a more precise manner. We use datasets collected on topics from the UK-EU Brexit referendum conducted in 2016. Each dataset discussed draws data from Twitter over a twelve-month period, from 1st September 2015 until 31st August 2016. Three data collection strategies are considered: collecting on human defined topic specific hashtags; collecting using a semi-automated technique to identify topic terms which are then used to collect tweets; and collecting from predefined users known to be tweeting on the topic. To investigate bias in the data we look at, and find wide variation in: group level metadata attributes such as size of the dataset; number of users in each set; average numbers of friends and followers; likely re-tweet status; and levels of inclusion of various add-ons such as hashtags, URLs and media. We also find that relevance to the topic differs between the sets; being far higher in the known users set. We investigate how readability of tweets within each set varies, particularly between known users and topic term sets. We also find that there is a surprising lack of overlap in the data obtained using different collection methods.
What Gets Media Attention and How Media Attention Evolves Over Time: Large-Scale Empirical Evidence from 196 Countries
Authors: An Jisun , Kwak Haewon
Published: November 2022

It is known that news topics, covered more frequently and over longer periods of time, are considered to be important to the public. Hence, what gets media attention and how media attention evolves over time has been studied for decades in communication study. However, previous studies are confined to a few countries or a few topics, mainly due to lack of longitudinal global data. In this work, we use a large-scale news data compiled from 196 countries to provide empirical analyses of media attention dynamics.
Suitable for All Ages: Using Reviews to Determine Appropriateness of Products
Authors: Daly Elizabeth , Alkan Oznur , Muller Michael
Published: November 2022

Product reviews provide insights in to real user experiences which can benefit others when making their purchasing decisions. Text-mining and NLP may be used to extract features and content that could influence a new user. Additionally, recommender systems and filtering interfaces rely on manufacturer reported data in order to support user preferences. In many instances this data may be absent or inaccurate. In this paper we focus on age related features mentioned in user reviews of baby and child related products in order to recommend the appropriate age range of a product. We demonstrate that manufacturer related information is frequently absent and when manufacturer specifications are available, we find they may not reflect real user experiences which could assist a buyer in their decision making process. As a result, we present a simple user interface to allow users assess the age appropriateness of the product.
What Comments Did I Get? How Post and Comment Characteristics Predict Interaction Satisfaction on Facebook
Authors: Sannon Shruti , Choi Yoon Hyung , Taft Jessie , Bazarova Natalya
Published: November 2022

Commenting on social media posts is an important part of interaction on Facebook and other social media sites. The number of comments people receive has been linked to their experience on social media, but less is known about how the content of comments relates to both the original posts and posters’ communicative outcomes, such as their mood and satisfaction with the interaction. The present study analyzed 822 Facebook status updates from a sample of 233 participants, along with 1175 corresponding comments, to determine how post, comment, and outcome characteristics are interrelated. We found congruence in valence and intimacy between status updates and comments, supporting the reciprocity principle of self-disclosure. Controlling for the number of comments and the content of posts, the content of comments mattered for satisfaction: more intimate comments were associated with higher levels of interaction satisfaction, and both intimacy and positive valence of comments were associated with more lasting effects on mood.
Changes in Social Media Behavior During Life Periods of Uncertainty
Authors: Page Xinru , Marabelli Marco
Published: November 2022

Social psychology has found that individuals are more self-focused in times of uncertainty such as new life phases. That is, in times of uncertainty and transition, one's thoughts and actions are more egocentric. In other literature, more narcissistic individuals have been shown to behave differently on social media. Bringing these two streams of research together, we investigate whether and how egocentrism during life periods of uncertainty affect social media behaviors. We identify two life phases with patterns of behavior driven by different types of narcissism. The social needs and heightened sense of egocentrism in these different life phases shape social media behavior, albeit in different ways. We discuss the relevance of these motivational and behavioral differences for understanding social media behavior in different phases of life.
Mapping Twitter Conversation Landscapes
Authors: Vosoughi Soroush , Vijayaraghavan Prashanth , Yuan Ann , Roy Deb
Published: November 2022

While the most ambitious polls are based on standardized interviews with a few thousand people, millions are tweeting freely and publicly in their own voices about issues they care about. This data offers a vibrant 24/7 snapshot of people's response to various events and topics. The sheer scale of the data on Twitter allows us to measure in aggregate how the various issues are rising and falling in prominence over time. However, the volume of the data also means that an intelligent tool is required to allow the users to make sense of the data. To this end, we built a novel, interactive web-based tool for mapping the conversation landscapes on Twitter. Our system utilizes recent advances in natural language processing and deep neural networks that are robust with respect to the noisy and unconventional nature of tweets, in conjunction with a scalable clustering algorithm an interactive visualization engine to allow users to tap the mine of information that is Twitter. We ran a user study with 40 participants using tweets about the 2016 US presidential election and the summer 2016 Orlando shooting, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can identify and make sense of the various conversation topics on Twitter.
Antagonism Also Flows Through Retweets: The Impact of Out-of-Context Quotes in Opinion Polarization Analysis
Authors: Guerra Pedro , Nalon Roberto , Assunção Renato , Meira Jr. Wagner
Published: November 2022

In this paper, we study the implications of the commonplace assumption that most social media studies make with respect to the nature of message shares (such as retweets) as a predominantly positive interaction. By analyzing two large longitudinal Brazilian Twitter datasets containing 5 years of conversations on two polarizing topics — Politics and Sports, we empirically demonstrate that groups holding antagonistic views can actually retweet each other more often than they retweet other groups. We show that assuming retweets as endorsement interactions can lead to misleading conclusions with respect to the level of antagonism among social communities, and that this apparent paradox is explained in part by the use of retweets to quote the original content creator out of the message's original temporal context, for humor and criticism purposes.
Data-Driven Approach to Measuring the Level of Press Freedom Using Media Attention Diversity from Unfiltered News
Authors: An Jisun , Kwak Haewon
Published: November 2022

Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environments worldwide, the current survey-based method has inherent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.
WhatApp Doc? A First Look at WhatsApp Public Group Data
Authors: Garimella Kiran , Tyson Gareth
Published: November 2022

In this dataset paper we describe our work on the collection and analysis of public WhatsApp group data. Our primary goal is to explore the feasibility of collecting and using WhatsApp data for social science research. We therefore present a generalisable data collection methodology, and a publicly available dataset for use by other researchers. To provide context, we perform statistical exploration to allow researchers to understand what public WhatsApp group data can be collected and how this data can be used. Given the widespread use of WhatsApp, our techniques to obtain public data and potential applications are important for the community.
Predicting PISA Scores from Students’ Digital Traces
Authors: Smirnov Ivan
Published: November 2022

The Programme for International Student Assessment (PISA) is an influential worldwide study that tests the skills and knowledge in mathematics, reading, and science of 15-year-old students. In this paper, we show that PISA scores of individual students can be predicted from their digital traces. We use data from the nationwide Russian panel study that tracks 4,400 participants of PISA and includes information about their activity on a popular social networking site. We build a simple model that predicts PISA scores based on students' subscriptions to various public pages on the social network. The resulting model can successfully discriminate between low- and high-performing students (AUC = 0.9). We find that top-performing students are interested in pages related to science and art, while pages preferred by low-performing students typically concern humor and horoscopes. The difference in academic performance between subscribers to such public pages could be equivalent to several years of formal schooling, indicating the presence of a strong digital divide. The ability to predict academic outcomes of students from their digital traces might unlock the potential of social media data for large-scale education research.
Leveraging Quality Prediction Models for Automatic Writing Feedback
Authors: Nilforoshan Hamed , Wu Eugene
Published: November 2022

User-generated, multi-paragraph writing is pervasive and important in many social media platforms (i.e. Amazon reviews, AirBnB host profiles, etc). Ensuring high-quality content is important. Unfortunately, content submitted by users is often not of high quality. Moreover, the characteristics that constitute high quality may even vary between domains in ways that users are unaware of. Automated writing feedback has the potential to immediately point out and suggest improvements during the writing process. Most approaches, however, focus on syntax/phrasing, which is only one characteristic of high-quality content. Existing research develops accurate quality prediction models. We propose combining these models with model explanation techniques to identify writing features that, if changed, will most improve the text quality. To this end, we develop a perturbation-based explanation method for a popular class of models called tree-ensembles. Furthermore, we use a weak-supervision technique to adapt this method to generate feedback for specific text segments in addition to feedback for the entire document. Our user study finds that the perturbation-based approach, when combined with segment-specific feedback, can help improve writing quality on Amazon (review helpfulness) and Airbnb (host profile trustworthiness) by > 14% (3X improvement over recent automated feedback techniques).
Tracing Community Genealogy: How New Communities Emerge from the Old
Authors: Tan Chenhao
Published: November 2022

The process by which new communities emerge is a central research issue in the social sciences. While a growing body of research analyzes the formation of a single community by examining social networks between individuals, we introduce a novel community-centered perspective. We highlight the fact that the context in which a new community emerges contains numerous existing communities. We reveal the emerging process of communities by tracing their early members' previous community memberships. Our testbed is Reddit, a website that consists of tens of thousands of user-created communities. We analyze a dataset that spans over a decade and includes the posting history of users on Reddit from its inception to April 2017. We first propose a computational framework for building genealogy graphs between communities. We present the first large-scale characterization of such genealogy graphs. Surprisingly, basic graph properties, such as the number of parents and max parent weight, converge quickly despite the fact that the number of communities increases rapidly over time. Furthermore, we investigate the connection between a community's origin and its future growth. Our results show that strong parent connections are associated with future community growth, confirming the importance of existing community structures in which a new community emerges. Finally, we turn to the individual level and examine the characteristics of early members. We find that a diverse portfolio across existing communities is the most important predictor for becoming an early member in a new community.
Distributed Knowledge in Crowds: Crowd Performance on Hidden Profile Tasks
Authors: Tausczik Yla , Boons Mark
Published: November 2022

Individuals today discuss information and form judgements as crowds in online communities and platforms. "Wisdom of the crowd" arguments suggest that, in theory, crowds have the capacity to bring together diverse expertise, pooling distributed knowledge and thereby solving challenging and complex problems. This paper concerns one way that crowds might fall short of this ideal. A large body of research in the social psychology of small groups concerns the shared information bias, a tendency for group members to focus on common knowledge at the expense of rarer information which only one or a few individuals might possess. We investigated whether this well-known bias for small groups also impacts larger crowds of 30 participants working on Amazon’s Mechanical Turk. We found that crowds failed to adequately pool distributed facts; that they were partially biased in how they shared facts; and that individual perception of group decisions was unstable. Nonetheless, we found that aggregating individual reports from the crowd resulted in moderate performance in solving the assigned task.
Media Bias Monitor: Quantifying Biases of Social Media News Outlets at Large-Scale
Authors: Ribeiro Filipe , Henrique Lucas , Benevenuto Fabricio , Chakraborty Abhijnan , Kulshrestha Juhi , Babaei Mahmoudreza , Gummadi Krishna
Published: November 2022

As Internet users increasingly rely on social media sites like Facebook and Twitter to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. Inferring the bias (or slant) of these media pages poses a difficult challenge for media watchdog organizations that traditionally rely on content analysis. In this paper, we explore a novel scalable methodology to accurately infer the biases of thousands of news sources on social media sites like Facebook and Twitter. Our key idea is to utilize their advertiser interfaces, that offer detailed insights into the demographics of the news source’s audience on the social media site. We show that the ideological (liberal or conservative) leaning of a news source can be accurately estimated by the extent to which liberals or conservatives are over-/under-represented among its audience. Additionally, we show how biases in a news source’s audience demographics, along the lines of race, gender, age, national identity, and income, can be used to infer more fine-grained biases of the source, such as social vs. economic vs. nationalistic conservatism. Finally, we demonstrate the scalability of our approach by building and publicly deploying a system, called "Media Bias Monitor", which makes the biases in audience demographics for over 20,000 news outlets on Facebook transparent to any Internet user.
Beyond Views: Measuring and Predicting Engagement in Online Videos
Authors: Wu Siqi , Rizoiu Marian-Andrei , Xie Lexing
Published: November 2022

The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.
The_Tower_of_Babel.jpg: Diversity of Visual Encyclopedic Knowledge Across Wikipedia Language Editions
Authors: He Shiqing , Lin Allen Yilun , Adar Eytan , Hecht Brent
Published: November 2022

Across all Wikipedia language editions, millions of images augment text in critical ways. This visual encyclopedic knowledge is an important form of wikiwork for editors, a critical part of reader experience, an emerging resource for machine learning, and a lens into cultural differences. However, Wikipedia research--and cross-language edition Wikipedia research in particular--has thus far been limited to text. In this paper, we assess the diversity of visual encyclopedic knowledge across 25 language editions and compare our findings to those reported for textual content. Unlike text, translation in images is largely unnecessary. Additionally, the Wikimedia Foundation, through the Wikipedia Commons, has taken steps to simplify cross-language image sharing. While we may expect that these factors would reduce image diversity, we find that cross-language image diversity rivals, and often exceeds, that found in text. We find that diversity varies between language pairs and content types, but that many images are unique to different language editions. Our findings have implications for readers (in what imagery they see), for editors (in deciding what images to use), for researchers (who study cultural variations), and for machine learning developers (who use Wikipedia for training models).