bluuebunny/crossref_metadata_2025 huggingface dataset:
How To Tell Head From Tail in User-Generated Content Corpora
Authors: Sastry Nishanth
Published: November 2022

This paper asks whether unpopular tail items in user-generated content corpora are important, and how tail items differ from the popular head items. We develop a user-centric characterisation of the tail which shows that although the head receives a disproportionate share of interest, tail items collectively serve a large number of users. "Tail seekers," with more like's in the tail than the head, are shown to constitute more than half the user base. We then examine how interests in head and tail items differ. Temporally, head items are found to enjoy a sustained interest, whereas interest in tail items is short lived. Spatially, interest in tail items is more geographically diverse. Finally, from a social angle, interest in unpopular items appears to be more "viral" than non-viral. We discuss implications of these observations for the handling and distribution of user-generated content.
What Sticks With Whom? Twitter Follower-Followee Networks and News Classification
Authors: Bastos Marco , Travitzki Rodrigo , Puschmann Cornelius
Published: November 2022

In this paper we analyze Twitter as a news channel in which the network of followers and followees significantly corresponds with the message content. We classified our data into twelve topics analogous to traditional newspaper sections and investigated whether the spread of information depended upon the Twitter network of followers and followees. To test this, we mapped the social network related to each topic and calculated the occurrence of retweet and mention mes-sages whose senders and receivers were interconnected as followers and followees. We found that on average 10% of retweets (RT-messages) and 5% of direct mentions between users (AT-messages) in Twitter hashtags are sent and received by users interconnected as followers and followees. These figures vary considerably from topic to topic, ranging from 15%-19% within Technology, Special Events and Politics to 3%-5% within the categories Personalities and Twitter-Idioms. The results show that hard-news messages are retweeted by a considerably larger community of users interconnected as followers and followees. We then performed a statistical correlation analysis of the dataset to validate the classification of hashtag in news sections based on retweet connectivity.
Visualization of the Public’s Opinion on Politically Influential Tweets
Authors: Kim Tanyoung , DiSalvo Carl
Published: November 2022

We present a Twitter-based website The Political Grid Project that focuses on harnessing the potential of Twitter as a political medium. On this site, the general public can create new datasets additionally to existing Twitter data, and investigate the data through diverse visualizations. This site lists tweets posted by political entities, and a user votes on each tweet based on the two standards—how much she agrees with the idea expressed in the tweet, and how important it is to her. More importantly, our site provides users with various interactive visualizations that represent both individual and collective voting data accompanied by data accessed from Twitter APIs. While exploring the visualizations, users can closely examine various aspects of politics, including their political preferences, the ties among the voted entities based on the analysis of the collective data,and temporal or geographical patterns. Ultimately, we expect our users to reflect on the political issues in tweets that are otherwise ephemeral and to better understand candidates in upcoming elections. Furthermore, our site allows users to investigate the public’s aggregated opinion and their political stance among the public.
Organizers
Authors: Archambault Daniel
Published: November 2022

List of organizers of the Social Media Visualization workshop.
Social Media and Citizen Engagement in a City-State: A Study of Singapore
Authors: Skoric Marko , Pan Ji , Poor Nathaniel
Published: November 2022

Social media plays an important role in the process of political engagement, especially in societies where significant constraints over traditional media and participation still exist. Little is known about how social media use is related to these constraints. This study examines how citizens’ perceptions of government control predict social media use and how this use is related to offline participation in the context of a city-state, Singapore. Based on a national survey of 2000 respondents, we found that perceptions of control over traditional media and political activity increase content production on social media and that perceived control of the mass media motivates citizens to consume political content on social media. Interestingly, perceptions of government control over the Internet reduced rather than increased social media production. More importantly, we find that social media use is related to a greater likelihood of offline citizen participation, namely attendance of political rallies. The findings suggest that social media alters the balance of power in the dependency relationships that exist between the government, media organizations and citizens, creating new venues for online political discourse which in turn help promote real-world political participation.
Using Social Media to Infer Gender Composition of Commuter Populations
Authors: Liu Wendy , Zamal Faiyaz , Ruths Derek
Published: November 2022

In order for a municipality to effectively service and engage its constituency, it must understand the composition of the communities within it. Up to the present, such demographic estimates for target populations have been obtained largely from census data or expensive, time-intensive surveys. In this paper, we use Twitter microblog content to estimate the gender makeup of commuting populations using different modes of transportation (cars, public transportation, and bikes) in Toronto, Canada. We apply a demographic inference algorithm to 33,215 public Twitter accounts that follow one of three popular transportation-related Twitter-based news feeds (one for traffic, one for public transportation updates, and one for bicycling). Recent census data provides ground truth against which to compare the estimates we derive from Twitter. We find that, for all three communities (car drivers, public transport users, and bicyclists), the estimates obtained from Twitter reflect the majority-minority relationships between genders reported in census data. This provides preliminary, but compelling evidence that Twitter may be a platform that can go beyond simply signaling the presence of physical communities to actually measure their compositions.
Preface
Authors: Daly Elizabeth
Published: November 2022

This workshop is interested in research that works towards closing the loop between the city and the citizens.
Twitris v3: From Citizen Sensing to Analysis, Coordination and Action
Authors: Purohit Hemant , Sheth Amit
Published: November 2022

Citizen sensing, with a billion plus active users and billion plus tweets/week, is complemented by shared information from contextually relevant Web of Data (blogs, news, and media objects) and background knowledge. How can these enable us in informing, understanding and managing a broad variety of activities and events locally and around the world? Twitris, currently in version 3, is a scalable and interactive platform which continuously collects, aggregates, integrates, and analyzes the above forms of data and knowledge to give deeper insights, as well as facilitate research and development on coordination and targeted actions related to any event. In this demonstration, we will show Twitris’ comprehensive capabilities in spatio-temporal-thematic, people-contentnetwork, and sentiment-emotion-subjectivity analyses, with examples from business intelligence including brand tracking and advertising campaigns, social/political unrests, and disaster events such as U.S. Election 2012, Occupy Wall Street (OWS) protest, Hurricane Sandy, etc. Visitors from diverse backgrounds will be able to play with the system for analyses of archived as well as live events during the demo.
Tutorials
Authors: Kiciman Emre
Published: November 2022

The tutorials presented at ICWSM 2013 included Advanced Methods for Collecting Social Science Data in the Social Media Field, presented by Riki Conrey; Information-Theoretic Tools for Social Media Analysis, presented by Greg Ver Steeg and Aram Galstyan; Crisis Mapping, Citizen Sensing, and Social Media Analytics: Leveraging Citizen Roles for Crisis Response, presented by Amit Sheth, Patrick Meier, Carlos Castillo, and Hemant Purohit; Multiple Network Models for Complex Online Social Network Analysis, presented by Matteo Magnani and Luca Rossi; and Pulse of Virtual Worlds: Behavioral Mining and SNA in Massive Online Games, presented by Muhammad Ahmad and Jaideep Srivastava.
Booming Up the Long Tails: Discovering Potentially Contributive Users in Community-Based Question Answering Services
Authors: Sung Juyup , Lee Jae-Gil , Lee Uichin
Published: November 2022

Community-based question answering (CQA) services such as Yahoo! Answers have been widely used by Internet users to get the answers for their inquiries. The CQA services totally rely on the contributions by the users. However, it is known that newcomers are prone to lose their interests and leave the communities. Thus, finding expert users in an early phase when they are still active is essential to improve the chances of motivating them to contribute to the communities further. In this paper, we propose a novel approach to discovering "potentially" contributive users from recently-joined users in CQA services. The likelihood of becoming a contributive user is defined by the user's expertise as well as availability, which we call the answer affordance. The main technical difficulty lies in the fact that such recently-joined users do not have abundant information accumulated for many years. We utilize a user's productive vocabulary to mitigate the lack of available information since the vocabulary is the most fundamental element that reveals his/her knowledge. Extensive experiments were conducted with a huge data set of Naver Knowledge-In (KiN), which is the dominating CQA service in Korea. We demonstrate that the top rankers selected by the answer affordance outperformed those by KiN in terms of the amount of answering activity.
Online Social Capital: Mood, Topical and Psycholinguistic Analysis
Authors: Nguyen Thin , Dao Bo , Phung Dinh , Venkatesh Svetha , Berk Michael
Published: November 2022

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.
A Multi-Indicator Approach for Geolocalization of Tweets
Authors: Schulz Axel , Hadjakos Aristotelis , Paulheim Heiko , Nachtwey Johannes , Mühlhäuser Max
Published: November 2022

Real-time information from microblogs like Twitter is useful for different applications such as market research, opinion mining, and crisis management. For many of those messages, location information is required to derive useful insights. Today, however, only around 1% of all tweets are explicitly geotagged. We propose the first multi-indicator method for determining (1) the location where a tweet was created as well as (2) the location of the user's residence. Our method is based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile. An evaluation shows that our method is capable of locating 92% of all tweets with a median accuracy of below 30km, as well as predicting the user's residence with a median accuracy of below 5.1km. With that level of accuracy, our approach significantly outperforms existing work.
That's What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships
Authors: Jurgens David
Published: November 2022

Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms.
Discovering Dedicators with Topic-Based Semantic Social Networks
Authors: Jang Jiyeon , Myaeng Sung-Hyon
Published: November 2022

Influential people are known to play a key role in diffusing information in a social network. When measuring influence in a social network, most studies have focused on the use of the graph topology representing a network. As a result, popular or famous people tend to be identified as influencers. While they have a potential to influence people with the network connections by propagating information to their friends or followers, it is not clear whether they can indeed serve as an influencer as expected, especially for specific topic areas. In this paper, we introduce the notion of dedicators, which measures the extent to which a user has dedicated to transmit information in selected topic areas to the people in their egocentric networks. To detect topic-based dedicators, we propose a measure that combines both community-level and individual-level factors, which are related to the volume and the engagement level of their conversations and the degree of focus on specific topics. Having analyzed a Twitter conversation data set, we show that dedicators are not co-related with topology-based influencers; users with high in-degree influence tend to have a low dedication level while top dedicators tend to have richer conversations with others, taking advantage of smaller and manageable social networks.
Understanding the Roles and Uses of Web Tutorials
Authors: Lafreniere Ben , Bunt Andrea , Lount Matthew , Terry Michael
Published: November 2022

In this paper we identify roles and uses of web-based tutorials through an examination of tutorials’ comments sections. Through this analytical lens, we find that web tutorials serve a variety of needs, providing: in-task help for users with an immediate, specific goal to accomplish; a means for users to proactively expand their repertoire of skills; and an opportunity for novices to shadow and experience an expert’s work practices. We also find a number of emergent practices in tutorial comments. Users post “help-me” stack traces, a type of comment useful for debugging tutorial content; use comments sections as opportunistic support forums; and turn to comments sections for social and technical validation of their personal skill sets. Collectively, these findings enrich existing perspectives on web-based tutorials and argue for new mechanisms to support these various use cases.
Efficient Clustering of Short Messages into General Domains
Authors: Tsur Oren , Littman Adi , Rappoport Ari
Published: November 2022

The ever increasing activity in social networks is mainly manifested by a growing stream of status updating or microblogging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inaccurate and inefficient due to sparseness. This paper presents an accurate and efficient algorithm for clustering Twitter tweets. We break the clustering task into two distinctive tasks/stages: (1) batch clustering of user annotated data, and (2) online clustering of a stream of tweets. In the first stage we rely on the habit of `tagging', common in social media streams (e.g. hashtags), thus the algorithm can bootstrap on the tags for clustering of a large pool of hashtagged tweets. The stable clusters achieved in the first stage lend themselves for online clustering of a stream of (mostly) tagless messages.We evaluate our results against gold-standard classification and validate the results by employing multiple clusteringevaluation measures (information theoretic, paired, F and greedy). We compare our algorithm to a number of otherclustering algorithms and various types of feature sets. Results show that the algorithm presented is both accurate andefficient and can be easily used for large scale clustering of sparse messages as the heavy lifting is achieved ona sublinear number of documents.
Towards Supporting Search over Trending Events with Social Media
Authors: Kairam Sanjay , Morris Meredith , Teevan Jaime , Liebling Dan , Dumais Susan
Published: November 2022

Many search engines identify bursts of activity around particular topics and reflect these back to users as Popular Now or Hot Searches. Activity around these topics typically evolves quickly in real-time during the course of a trending event. Users’ informational needs when searching for such topics will vary depending on the stage at which they engage with an event. Through a survey and log study, we observe that interaction with content about trending events varies significantly with prior awareness of the event. Building on this observation, we conduct a larger-scale analysis of query logs and social media data associated with hundreds of trending events. We find that search and social media activity tend to follow similar temporal patterns, but that social media activity leads by a few hours. While user interest in trending event content predictably diverges during peak activity periods, the overlap between content searched and shared increases. We discuss how these findings relate to the design of interfaces to better support sensemaking around trending events by integrating real-time social media content with traditional search results.
Predicting Depression via Social Media
Authors: De Choudhury Munmun , Gamon Michael , Counts Scott , Horvitz Eric
Published: November 2022

Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.
Friendship Paradox Redux: Your Friends Are More Interesting Than You
Authors: Hodas Nathan , Kooti Farshad , Lerman Kristina
Published: November 2022

Feld's friendship paradox states that "your friends have more friends than you, on average." This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends. Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98% of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you. This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers. In addition, we discover two new paradoxes: the virality paradox that states your friends receive more viral content than you, on average, and the activity paradox, which states your friends are more active than you, on average." The latter paradox is important in regulating online communication.It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly.While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.
Leveraging Browsing Patterns for Topic Discovery and Photostream Recommendation
Authors: Chiarandini Luca , Grabowicz Przemyslaw , Trevisiol Michele , Jaimes Alejandro
Published: November 2022

In photo-sharing websites and in social networks, photographs are most often browsed as a sequence: users who view a photo are likely to click on those that follow. The sequences of photos (which we call photostreams), as opposed to individual images, can therefore be considered to be very important content units in their own right. In spite of their importance, those sequences have received little attention even though they are at the core of how people consume image content. In this paper, we focus on photostreams. First, we perform an analysis of a large dataset of user logs containing over 100 million pageviews, examining navigation patterns between photostreams. Based on observations from the analysis, we build a stream transition graph to analyze common stream topic transitions (e.g., users often view “train” photostreams followed by “firetruck” photostreams). We then implement two stream recommendation algorithms, based on collaborative filtering and on photo tags, and report the results of a user study involving 40 participants. Our analysis yields interesting insights into how people navigate between photostreams, while the results of the user study provide useful feedback for evaluating the performance and characteristics of the recommendation algorithms.
Don’t Be Spoiled by Your Friends: Spoiler Detection in TV Program Tweets
Authors: Jeon Sungho , Kim Sungchul , Yu Hwanjo
Published: November 2022

Providing a convenient mechanism for accessing the Internet, smartphones have led to the rapid growth of Social Networking Services (SNSs) such as Twitter and have served as a major platform for SNSs. Nowadays, people are able to check conveniently the SNS messages posted by their friends and followers via their smartphones. As a consequence, people are exposed to spoilers of TV programs that they follow. So far, there are two previous works that explored the detection of spoilers in texts, not SNS: (1) keyword matching method and (2) machine-learning method based on Latent Dirichlet Allocation (LDA). The keyword matching method evaluates most tweets as spoilers; hence its poor recall performance. The other method based on LDA, although successful on large text, works poorly on short segments of text such as those found on Twitter and evaluates most tweets as non-spoilers. This paper presents four features that are significant in the classification of spoiler tweets. Using those features, we classified spoiler tweets pertaining to a reality TV show (“Dancing with the Stars”). We experimentally compared our method with previous methods, with our method achieving substantially higher precision compared to the keyword matching and LDA-based methods while maintaining comparable recalls.
Towards Predicting the Best Answers in Community-based Question-Answering Services
Authors: Tian Qiongjie , Zhang Peng , Li Baoxin
Published: November 2022

Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.
Personality Traits Recognition on Social Network - Facebook
Authors: Alam Firoj , A. Stepanov Evgeny , Riccardi Giuseppe
Published: November 2022

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.
Workshop on Computational Personality Recognition: Shared Task
Authors: Celli Fabio , Pianesi Fabio , Stillwell David , Kosinski Michal
Published: November 2022

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.
Using Nuances of Emotion to Identify Personality
Authors: Mohammad Saif , Kiritchenko Svetlana
Published: November 2022

Past work on personality detection has shown that frequency of lexical categories such as first person pronouns, past tense verbs, and sentiment words have significant correlationswith personality traits.  In this paper, for the first time, we show that fine affect(emotion) categories such as that of excitement, guilt, yearning, and admiration aresignificant indicators of personality.  Additionally, we perform experiments to show thatthe gains provided by the fine affect categories are not obtained by using coarse affectcategories alone or with specificity features alone.  We employ these features in five SVMclassifiers for detecting five personality traits through essays.  We find that the use offine emotion features leads to statistically significant improvement over a competitivebaseline, whereas the use of coarse affect and specificity features does not.
Too Neurotic, Not Too Friendly: Structured Personality Classification on Textual Data
Authors: Iacobelli Francisco , Culotta Aron
Published: November 2022

Personality plays a fundamental role in human interaction. With the increasing amount of online user-generated content, automatic detection of a person's personality based on the text she produces is an important step to labeling and analyzing human behavior at a large scale. To date, most approaches to personality classification have modeled each personality trait in isolation (e.g., independent binary classification). In this paper, we instead model the dependencies between different personality traits using conditional random fields. Our study finds a correlation between Agreeableness and Emotional Stability traits that can improve Agreeableness classification. However, we also find that accuracy on other traits can degrade with this approach, due in part to the overall problem difficulty.
The Car that Hit The Burning House: Understanding Small Scale Incident Related Information in Microblogs
Authors: Schulz Axel , Ristoski Petar
Published: November 2022

Microblogs are increasingly gaining attention as an important information source in emergency management. In this case, state-of-the-art has shown that many valuable situational information is shared by citizens and official sources. However, current approaches focus on information shared during large scale incidents, with high amount of publicly available information. In contrast, in this paper, we conduct two studies on every day small scale incidents. First, we propose the first machine learning algorithm to detect three different types of small scale incidents with a precision of 82.2% and 82% recall. Second, we manually classify users contributing situational information about small scale incidents and show that a variety of individual users publish incident related information. Furthermore, we show that those users are reporting faster than official sources
Making Sense of Cities Using Social Media: Requirements for Hyper-Local Data Aggregation Tools
Authors: Schwartz Raz , Naaman Mor , Matni Ziad
Published: November 2022

As more people tweet, check-in and share pictures and videos of their daily experiences in the city, new opportunities arise to understand urban activity. When aggregated, these data can uncover invaluable local insights for local stakeholders such as journalists, first responders and city officials. To better understand the needs and requirements for this kind of aggregation tools, we perform an exploratory study that includes interviews with 12 domain experts that utilize local information on a daily basis. Our results shed light on current practices, existing tools and unfulfilled needs of these professionals. We use these findings to discuss the requirements for hyper-local social media data aggregation tools for the study of cities on a large scale. We outline a list of key features that can better serve the discovery of patterns and insights about both real-time activity and historical perspectives of local communities. As more people tweet, check-in and share pictures and videos of their daily experiences in the city, new opportunities arise to understand urban activity. When aggregated, these data can uncover invaluable local insights for local stakeholders such as journalists, first responders and city officials. To better understand the needs and requirements for this kind of aggregation tools, we perform an exploratory study that includes interviews with 12 domain experts that utilize local information on a daily basis. Our results shed light on current practices, existing tools and unfulfilled needs of these professionals. We use these findings to discuss the requirements for hyper-local social media data aggregation tools for the study of cities on a large scale. We outline a list of key features that can better serve the discovery of patterns and insights about both real-time activity and historical perspectives of local communities.
Preface
Authors: Daly Elizabeth , Schwartz Raz
Published: November 2022

Social media analysis can play a key role in providing insights into people's activities, opinions and day-to-day lives. When they are geolocated, these user-generated information streams become a unique opportunity to understand the rhythms and tenors of a city and its citizens. By applying computational, social science, and humanities methods to social media data such as photos, tweets and check-ins, researchers are now beginning to conceive of new methodological and theoretical frameworks not only to extract local insights but, more importantly, to better understand cities and their residents. This workshop aims to understand the various ways in which social media data can be used to produce knowledge about cities that supports citizen engagement.
Of Pins and Tweets: Investigating How Users Behave Across Image- and Text-Based Social Networks
Authors: Ottoni Raphael , Las Casas Diego , Pesce João Paulo , Meira Jr. Wagner , Wilson Christo , Mislove Alan , Almeida Virgilio
Published: November 2022

Today, it is the norm for online social (OSN) users to have accounts on multiple services. For example, a recent study showed that 34% of all Twitter users also use Pinterest. This situation leads to interesting questions such as: Are the activities that users perform on each site disjoint? Alternatively, if users perform the same actions on multiple sites, where does the information originate? Given the interlinking between social networks, failure to understand activity across multiple sites may obfuscate the true information dissemination dynamics of the social web. In this study, we take the first steps towards a more complete understanding of user behavior across multiple OSNs. We collect a sample of over 30,000 users that have accounts on both Twitter and Pinterest, crawling their profile information and activity on a daily basis for a period of almost three months. We develop a novel methodology for comparing activity across these two sites. We find that the global patterns of use across the two sites differ significantly, and that users tend to post items to Pinterest before posting them on Twitter. Our findings can inform the understanding of the behavior of users on individual sites, as well as the dynamics of sharing across the social web.
Personalizing Forum Search using Multidimensional Random Walks
Authors: Ganu Gayatree , Marian Amélie
Published: November 2022

Online forums are a vital resource for users to ask questions and to participate in discussions. Yet, the search functionality on such forum sites is very primitive; posts containing the searched keywords are retrieved in the order of their creation date. In these interactive and social web forum sites, users frequently make connections with other users due to shared interests, same information needs or similar profiles. A critical challenge then, is to score and rank the forum posts while taking into account these relations between users. In this paper, we present a personalized search over forums that leverages user similarities developed via multiple relations linking users. We build a novel multidimensional random walk model that uniformly incorporates the heterogeneous user relations to find similar forum participants. We then use this multi-relational user similarity to predict future interactions by personalizing answer search. Furthermore, we extend our methods to enhance keyword search for forum readers, by using expertise scores for all existing forum participants. Our results show that by leveraging the author dimension we can retrieve more relevant results than the traditional IR scoring alone.
CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises
Authors: Olteanu Alexandra , Castillo Carlos , Diaz Fernando , Vieweg Sarah
Published: November 2022

Locating timely, useful information during crises and mass emergencies is critical for those forced to make potentially life-altering decisions. As the use of Twitter to broadcast useful information during such situations becomes more widespread, the problem of finding it becomes more difficult. We describe an approach toward improving the recall in the sampling of Twitter communications that can lead to greater situational awareness during crisis situations. First, we create a lexicon of crisis-related terms that frequently appear in relevant messages posted during different types of crisis situations. Next, we demonstrate how we use the lexicon to automatically identify new terms that describe a given crisis. Finally, we explain how to efficiently query Twitter to extract crisis-related messages during emergency events. In our experiments, using a crisis lexicon leads to substantial improvements in terms of recall when added to a set of crisis-specific keywords manually chosen by experts; it also helps to preserve the original distribution of message types.
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text
Authors: Hutto C. , Gilbert Eric
Published: November 2022

The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.
You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare
Authors: Silva Thiago , Vaz de Melo Pedro , Almeida Jussara , Musolesi Mirco , Loureiro Antonio
Published: November 2022

Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users' cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions.
Get Back! You Don’t Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations
Authors: Hannak Aniko , Margolin Drew , Keegan Brian , Weber Ingmar
Published: November 2022

The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and FactCheck.org, are a strategy users can employ to refute false claims made by their peers. While laboratory research suggests such interventions are not effective in persuading people to abandon false ideas, little work considers how such interventions are actually deployed in real-world conversations. Using approximately 1,600 interventions observed on Twitter between 2012 and 2013, we examine the contexts and consequences of fact-checking interventions.We focus in particular on the social relationship between the individual who issues the fact-check and the individual whose facts are challenged. Our results indicate that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends. Finally, we discuss implications for designing more effective interventions against misinformation.
What We Instagram: A First Analysis of Instagram Photo Content and User Types
Authors: Hu Yuheng , Manikonda Lydia , Kambhampati Subbarao
Published: November 2022

Instagram is a relatively new form of communication where users can easily share their updates by taking photos and tweaking them using filters. It has seen rapid growth in the number of users as well as uploads since it was launched in October 2010. In spite of the fact that it is the most popular photo capturing and sharing application, it has attracted relatively less attention from the research community. In this paper, we present both qualitative and quantitative analysis on Instagram. We use computer vision techniques to examine the photo content. Based on that, we identify the different types of active users on Instagram using clustering. Our results reveal several insights about Instagram which were never studied before, that include: 1) Eight popular photos categories, 2) Five distinct types of Instagram users in terms of their posted photos, and 3) A user's audience (number of followers) is independent of his/her shared photos on Instagram. To our knowledge, this is the first in-depth study of content and users on Instagram.
Antisocial Behavior in Online Discussion Communities
Authors: Cheng Justin , Danescu-Niculescu-Mizil Cristian , Leskovec Jure
Published: November 2022

User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.
Making Use of Derived Personality: The Case of Social Media Ad Targeting
Authors: Chen Jilin , Haber Eben , Kang Ruogu , Hsieh Gary , Mahmud Jalal
Published: November 2022

People’s social media text has been shown to have limited prediction power of their personality. In this work, we conducted a survey study and a field study to explore the feasibility of using predicted personality traits derived from socialmedia text for the purpose of ad targeting. In the survey study, we measured people's personalities and their responses to an advertisement tweet. We found that people withhigh openness and low neuroticism responded more favorably to a targeted advertisement, thus demonstrating the effectsof the personality traits themselves. In the field study,we sent the advertisement tweets to real-world Twitter users,and found the same effects on users' responses using personality traits derived from users' tweet text. These results suggest that the derived personality traits had the same effects as the personality traits measured by traditional personality questionnaires, and can indeed improve ad targeting in real-world settings.
Linguistic Bias in Collaboratively Produced Biographies: Crowdsourcing Social Stereotypes?
Authors: Otterbacher Jahna
Published: November 2022

Language is the primary medium through which stereotypes are conveyed. Even when we avoid using derogatory language, there are many subtle ways in which stereotypes are created and reinforced, and they often go unnoticed. Linguistic bias, the systematic asymmetry in language patterns as a function of the social group of the persons described, may play a key role. We ground our study in the social psychology literature on linguistic biases, and consider two ways in which biases might manifest: through the use of more abstract versus concrete language, and subjective words. We analyze biographies of African American and Caucasian actors at the Internet Movie Database (IMDb), hypothesizing that language patterns vary as a function of race and gender. We find that both attributes are correlated to the use of abstract, subjective language. Theory predicts that we describe people and scenes that are expected, as well as positive aspects of our in-group members, with more abstract language. Indeed, white actors are described with more abstract, subjective language at IMDb, as compared to other social groups. Abstract language is powerful because it implies stability over time; studies have shown that people have better impressions of others described in abstract terms. Therefore, the widespread prevalence of linguistic biases in social media stands to reinforce social stereotypes. Further work should consider the technical and social characteristics of the collaborative writing process that lead to an increase or decrease in linguistic biases.