bluuebunny/crossref_metadata_2025 huggingface dataset:
The Life of the Party: Impact of Social Mapping in OpenStreetMap
Authors: Hristova Desislava , Quattrone Giovanni , Mashhadi Afra , Capra Licia
Published: November 2022

OpenStreetMap has grown rapidly into an exemplary geowiki, where contributors collectively build an open map of the world. Official ‘mapping party’ events are organized on a regular basis to invite users to socialize, map and to engage new-comers locally. Here, we measure the direct and indirect impact of mapping parties on user contributions in both the short and long terms. We question whether this social mapping is a cause for users to become highly committed through a social bond, or is an effect of the mappers’ need to find a common social ground. We show that mapping parties have distinct effects on different types of users, with a more profound direct impact on weaker contributors and a longer term effect on heavy contributors.
Experiments on Motivational Feedback for Crowdsourced Workers
Authors: Lee Tak Yeon , Dugan Casey , Geyer Werner , Ratchford Tristan , Rasmussen Jamie , Shami N. Sadat , Lupushor Stela
Published: November 2022

This paper examines the relationship between motivational design and its longitudinal effects on crowdsourcing systems. In the context of a company internal web site that crowdsources the identification of Twitter accounts owned by company employees, we designed and investigated the effects of various motivational features including individual / social achievements and gamification. Our 6-month experiment with 437 users allowed us to compare the features in terms of both quantity and quality of the work produced by participants over time. While we found that gamification can increase workers’ motivation overall, the combination of motivational features also matters. Specifically, gamified social achievement is the best performing design over a longer period of time. Mixing individual and social achievements turns out to be less effective and can even encourage users to game the system.
Encouraging Reading of Diverse Political Viewpoints with a Browser Widget
Authors: Munson Sean , Lee Stephanie , Resnick Paul
Published: November 2022

The Internet gives individuals more choice in political news and information sources and more tools to filter out disagreeable information. Citing the preference described by selective exposure theory — people prefer information that supports their beliefs and avoid counter-attitudinal information — observers warn that people may use these tools to access only agreeable information and thus live in ideological echo chambers. We report on a field deployment of a browser extension that showed users feedback about the political lean of their weekly and all time reading behaviors. Compared to a control group, showing feedback led to a modest move toward balanced exposure, corresponding to 1-2 visits per week to ideologically opposing sites or 5-10 additional visits per week to centrist sites.
The Anatomy of Large Facebook Cascades
Authors: Dow P. Alex , Adamic Lada , Friggeri Adrien
Published: November 2022

When users post photos on Facebook, they have the option of allowing their friends, followers, or anyone at all to subsequently reshare the photo. A portion of the billions of photos posted to Facebook generates cascades of reshares, enabling many additional users to see, like, comment, and reshare the photos. In this paper we present characteristics of such cascades in aggregate, finding that a small fraction of photos account for a significant proportion of reshare activity and generate cascades of non-trivial size and depth. We also show that the true influence chains in such cascades can be much deeper than what is visible through direct attribution. To illuminate how large cascades can form, we study the diffusion trees of two widely distributed photos: one posted on President Barack Obama’s page following his reelection victory, and another posted by an individual Facebook user hoping to garner enough likes for a cause. We show that the two cascades, despite achieving comparable total sizes, are markedly different in their time evolution, reshare depth distribution, predictability of subcascade sizes, and the demographics of users who propagate them. The findings suggest not only that cascades can achieve considerable size but that they can do so in distinct ways.
Classifying Political Orientation on Twitter: It’s Not Easy!
Authors: Cohen Raviv , Ruths Derek
Published: November 2022

Numerous papers have reported great success at inferring the political orientation of Twitter users. This paper has some unfortunate news to deliver: while past work has been sound and often methodologically novel, we have discovered that reported accuracies have been systemically overoptimistic due to the way in which validation datasets have been collected, reporting accuracy levels nearly 30% higher than can be expected in populations of general Twitter users. Using careful and novel data collection and annotation techniques, we collected three different sets of Twitter users, each characterizing a different degree of political engagement on Twitter - from politicians (highly politically vocal) to "normal" users (those who rarely discuss politics). Applying standard techniques for inferring political orientation, we show that methods which previously reported greater than 90% inference accuracy, actually achieve barely 65% accuracy on normal users. We also show that classifiers cannot be used to classify users outside the narrow range of political orientation on which they were trained. While a sobering finding, our results quantify and call attention to overlooked problems in the latent attribute inference literature that, no doubt, extend beyond political orientation inference: the way in which datasets are assembled and the transferability of classifiers.
Unexpected Relevance: An Empirical Study of Serendipity in Retweets
Authors: Sun Tao , Zhang Ming , Mei Qiaozhu
Published: November 2022

Serendipity is a beneficial discovery that happens in an unexpected way. It has been found spectacularly valuable in various contexts, including scientific discoveries, acquisition of business, and recommender systems. Although never formally proved with large-scale behavioral analysis, it is believed by scientists and practitioners that serendipity is an important factor of positive user experience and increased user engagement. In this paper, we take the initiative to study the ubiquitous occurrence of serendipitious information diffusion and its effect in the context of microblogging communities. We refer to serendipity as unexpected relevance, then propose a principled statistical method to test the unexpectedness and the relevance of information received by a microblogging user, which identifies a serendipitous diffusion of information to the user. Our findings based on large-scale behavioral analysis reveal that there is a surprisingly strong presence of serendipitous information diffusion in retweeting, which accounts for more than 25% of retweets in both Twitter and Weibo. Upon the identification of serendipity, we are able to conduct observational analysis that reveals the benefit of serendipity to microblogging users. Results show that both the discovery and provision of serendipity increase the level of user activities and social interactions, while the provision of serendipitous information also increases the influence of Twitter users.
Calling All Facebook Friends: Exploring Requests for Help on Facebook
Authors: Ellison Nicole , Gray Rebecca , Vitak Jessica , Lampe Cliff , Fiore Andrew
Published: November 2022

Past research suggests Facebook use is linked to perceptions of social capital, a concept that taps into the resources people gain from interactions with their social network. In this study, we examine a sample of public Facebook status updates (N=20,000) for instances in which users request a response from their network. These attempts to mobilize resources offer insight into the mechanisms through which Facebook is used for social capital conversion. After identifying mobilization requests (N=856), we categorize them by cost (i.e., effort needed to satisfy the request) and type (e.g., opinion, information, social coordination) in order to describe the prevalence of these requests and the extent to which they require effort on the part of the potential responders. Finally, we examine characteristics of these users and the linguistic characteristics of status updates that contain mobilization requests.
Dude, srsly?: The Surprisingly Formal Nature of Twitter's Language
Authors: Hu Yuheng , Talamadupula Kartik , Kambhampati Subbarao
Published: November 2022

Twitter has become the de facto information sharing and communication platform. Given the factors that influence language on Twitter - size limitation as well as communication and content-sharing mechanisms - there is a continuing debate about the position of Twitter's language in the spectrum of language on various established mediums. These include SMS and chat on the one hand (size limitations) and email (communication), blogs and newspapers (content sharing) on the other. To provide a way of determining this, we propose a computational framework that offers insights into the linguistic style of all these mediums. Our framework consists of two parts. The first part builds upon a set of linguistic features to quantify the language of a given medium. The second part introduces a flexible factorization framework, soclin, which conducts a psycholinguistic analysis of a given medium with the help of an external cognitive and affective knowledge base. Applying this analytical framework to various corpora from several major mediums, we gather statistics in order to compare the linguistics of Twitter with these other mediums via a quantitative comparative study. We present several key insights: (1) Twitter's language is surprisingly more conservative, and less informal than SMS and online chat; (2) Twitter users appear to be developing linguistically unique styles; (3) Twitter's usage of temporal references is similar to SMS and chat; and (4) Twitter has less variation of affect than other more formal mediums. The language of Twitter can thus be seen as a projection of a more formal register into a size-restricted space.
Measuring and Summarizing Movement in Microblog Postings
Authors: Ruiz Eduardo , Hristidis Vagelis , Castillo Carlos , Gionis Aristides
Published: November 2022

Every day, users publish hundreds of millions of microblog postings in popular social-networking platforms such as Twitter and Facebook. When considered in aggregation, microblog postings have been shown to exhibit temporal patterns that reflect events of global significance. In this paper, we propose techniques to identify and quantify spatial patterns: for instance, a hashtag that is popular in one city on a given day, may become popular in a different city on the next day. Detecting these patterns is challenging given that the data are noisy and posts are not physically moving, i.e., they are not continuous trajectories in space like vehicles. Second, we introduce a multi-granular summarization model to describe the movement of a hashtag between two time periods. For interpretability, we seek representations of spatial changes that follow natural or administrative boundaries on a map, such as cities and states. We compare various movement measures using quantitative approaches and user surveys. We evaluate our movement summarization schemes by analytical loss and coverage functions. Our results show that it is possible to reliably detect relevant spatial changes automatically, and to produce simple summaries that represent accurately these changes.
Can Twitter Replace Newswire for Breaking News?
Authors: Petrovic Sasa , Osborne Miles , McCreadie Richard , Macdonald Craig , Ounis Iadh , Shrimpton Luke
Published: November 2022

Twitter is often considered to be a useful source of real-time news, potentially replacing newswire for this purpose. But is this true? In this paper, we examine the extent to which news reporting in newswire and Twitter overlap and whether Twitter often reports news faster than traditional newswire providers. In particular, we analyse 77 days worth of tweet and newswire articles with respect to both manually identified major news events and larger volumes of automatically identified news events. Our results indicate that Twitter reports the same events as newswire providers, in addition to a long tail of minor events ignored by mainstream media. However, contrary to popular belief, neither stream leads the other when dealing with major news events, indicating that the value that Twitter can bring in a news setting comes predominantly from increased event coverage, not timeliness of reporting.
Mood and Weather: Feeling the Heat?
Authors: Park Kunwoo , Lee Seonggyu , Kim Eunae , Park Minjee , Park Juyong , Cha Meeyoung
Published: November 2022

The notion that weather plays a significant role in determininga person’s mood is empirically beyond doubt, but quantifyingthe relationship on a large scale has traditionally beenhampered by difficulties in measuring people’s sentiments. Inthis paper we present the relationship between weather andthe U.S. residents’ sentiments from Twitter data and nationwidemeteorological records. We measure how temperature,humidity, and atmospheric pressure correlate with people’smoods, demonstrating the potential of large-scale online datain humanities fields such as psychology. We also providethese results with infographics, which can help the public understandscientific results easily.
Predicting the Audience Size of a Tweet
Authors: Kupavskii Andrey , Umnov Alexey , Gusev Gleb , Serdyukov Pavel
Published: November 2022

We study information dissemination in Twitter. We present an analysis of two important characteristics of so called retweet cascades: retweet count and show count, i.e., number of users that receive the tweet in their feed. We show that these two measures behave differently. We describe three models that are aimed to predict the audience size of a tweet: first one utilizes only the data available at the moment of the initial tweet, the second one utilizes the spread of the cascade up to some moment while the third one is an online prediction.
Friends, Strangers, and the Value of Ego Networks for Recommendation
Authors: Sharma Amit , Gemici Mevlana , Cosley Dan
Published: November 2022

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.
Detecting Chinese Wish Messages in Social Media: An Empirical Study
Authors: Chang George , Huang Han-Shen , Hsu Jane
Published: November 2022

People have wishes and sometimes share their wishes in social media, hoping to receive supports or to find partners with the same wishes. By collecting and analyzing those wishes, we may find out not only the trend of common wishes, but also the needs of individuals. This paper presents a preliminary study of Chinese wish detection. We provide analysis on the data from Linkwish, which is a micro social network for wish sharing with users mainly from Taiwan, Hong Kong, and Macao. Then, we use SVM with various types of features to classify these messages as wish or not. Our experimental results show that some features can achieve average areas under precision-recall curves higher than 0:95 in 10-fold cross validation.
Visualizing the "Pulse" of World Cities on Twitter
Authors: Rios Miguel , Lin Jimmy
Published: November 2022

We present a large-scale analysis of activity on Twitter in 50 majorcities around the world throughout all of 2012. Our study consists oftwo parts: First, we created heatmap visualizations, through whichperiods of comparatively intense and sparse activity are readilyapparent — these visual patterns reflect diurnal cycles, culturalnorms, and even religious practices. Second, we performed a clusteranalysis of these activity patterns to identify groupings of citiesthat are similar in the ways their inhabitants use Twitter. Notsurprisingly, cities cluster geographically, although we are able toidentify cross-cultural similarities as well.
Towards Automated Personality Identification Using Speech Acts
Authors: Appling Darren , Briscoe Erica , Hayes Heather , Mappus Rudolph
Published: November 2022

The way people communicate — be it verbally, visually, or via text– is indicative of personality traits. In social media the concept of the status update is used for individuals to communicate to their social networks in an always-on fashion. In doing so individuals utilize various kinds of speech acts that, while primarily communicating their content, also leave traces of their personality dimensions behind. We human-coded a set of Facebook status updates from the myPersonality dataset in terms of speech acts label and then experimented with surface level linguistic features including lexical, syntactic, and simple sentiment detection to automatically label status updates as their appropriate speech act. We apply supervised learning to the dataset and using our features are able to classify with high accuracy two dominant kinds of acts that have been found to occur in social media. At the same time we used the coded data to perform a regression analysis to determine which speech acts are significant of certain personality dimensions. The implications of our work allow for automatic large-scale personality identification through social media status updates.
Predicting Conscientiousness through Semantic Analysis of Facebook Posts
Authors: Tomlinson Marc , Hinote David , Bracewell David
Published: November 2022

Here we present a method for detecting an individual's level of conscientiousness based on an analysis of the content of their Facebook status updates. Our model is based on the identification of semantic evidence of facets related to conscientiousness; an individual's belief of their control over events around them and their goal orientation. The model achieves a correlation of r=.27 on a subset of the Facebook data published for the myPersonality workshop, with an accuracy of 58.13% for detecting if an individual is above or below the median and 68.03% for those outside of one standard deviation. While we take a narrow approach and identify only one personality trait, the general methodology of directly looking for evidence of traits in an individual's utterances is applicable to discovering models for all of the personality traits.
Predicting and Recommending Skills in the Social Enterprise
Authors: Varshney Kush , Wang Jun , Mojsilović Aleksandra , Fang Dongping , Bauer John
Published: November 2022

In this paper, we discuss the need for accurate skills assessments of employees in large, global, client-facing enterprises and shortcomings of existing systems for obtaining and managing expertise. We describe enterprise and social data that can be mined to improve skill assessment processes. We propose a matrix completion approach with side information for improved skill assessment prediction and recommendation, and discuss how outputs can improve existing business process use cases and illuminate new ones.
Voice of the Employees Resonated in Online Bamboo Forests
Authors: Park Kunwoo , Park Jaram , Park SungKyu , Kim Jaewoo , Kwon Sejeong , Kwak Jinah , Cha Meeyoung
Published: November 2022

While employee voice has been realized a critical element of organizational success, many employees often remain silent and withdraw useful information and ideas. In this paper,we present how employees utilize existing social media platform in a creative, collective way to anonymously share their voices—a phenomenon called the bamboo forests in Korea. We present the key characteristics of bamboo forests including self-mention and connectivity, and discuss the main topics and sentiments of such bamboo forests. Our analysis indicates that employees find such anonymous platform useful. We provide insights on the potential for utilizing the voice ofthe employees online.
Grassroots Innovation and Collaboration through Enterprise Crowdfunding
Authors: Muller Michael , Geyer Werner , Soule Todd , Steven Daniels
Published: November 2022

We describe a first experiment in enterprise crowdfunding –i.e., employees allocating money for employee-initiated proposals in an Intranet site, including a trial of this system with 511 employees in a large multinational company. Major outcomes include employee proposals addressing diverse individual and organizational needs, high participation rates, extensive inter-departmental, and the development of goals and motivations based on collective concerns.
Matching Data and Interpretation: Towards a Rosetta Stone Joining Behavioral and Survey Data
Authors: Margolin Drew , Lin Yu-Ru , Brewer Devon , Lazer David
Published: November 2022

This position paper argues for the establishment of a "Rosetta Stone" that maps behavioral signatures of population behavior to meaningful social categories. We propose a method for accomplishing this through the deployment of behavioral tracking combined with survey questionnaires. The goalis to identify salient patterns of behavioral activity and ask subjects to "explain" these patterns through their survey responses. As a case study, we provide analyses regarding calling patterns related to emergencies, such as bombings and hurricanes.
Magnet News: You Choose the Polarity of What You Read
Authors: Reis Julio , Gonçalves Pollyanna , Olmo Pedro , Prates Raquel , Benevenuto Fabrício
Published: November 2022

When was the last time you read a newspaper and was bombarded with articles you would rather not  see? Current news media shows massive number of news every day. But from tragedies to happy stories, people might want to choose to read only those articles that fit their current mood. The purpose of this project is to present the Magnet News, a Web tool in which users can choose if they want to see positive or negative news. Our system monitors the sentiment of news from important newspapers using the SentiStrength, a sentiment method proposed by literature that has been proved to be efficient in previous analysis.
Accessing Social Support and Overcoming Judgment on Social Media among Parents of Children with Special Needs
Authors: Ammari Tawfiq , Schoenebeck Sarita , Morris Meredith
Published: November 2022

Over 10 million children are diagnosed with a special need in the United States each year. For their parents, the economic and emotional costs can be overwhelming. Using a mixed methods approach, we show that parents of children with special needs rely primarily on Facebook pages, Facebook groups, and Yahoo! groups for accessing information and social support. Specifically, these groups offer geographic communities for local needs (e.g. school services) and case-based communities for specific conditions (e.g. autism). Promisingly, parents perceive less judgment online than offline when talking about their children’s special needs; however, these perceptions are nuanced. In particular, posts containing humor, achievement, or treatment suggestions are perceived to be more socially appropriate than posts containing judgment, violence, or social comparisons. However, results show that social media generally fails at connecting special needs families over time and across the life span. We discuss implications for social media site design and for supporting special needs families.
Identifying Purchase Intent from Social Posts
Authors: Gupta Vineet , Varshney Devesh , Jhamtani Harsh , Kedia Deepam , Karwa Shweta
Published: November 2022

In present times, social forums such as Quora and Yahoo! Answers constitute powerful media through which people discuss on a variety of topics and express their intentions and thoughts. Here they often reveal their potential intent to purchase - 'Purchase Intent' (PI). A purchase intent is defined as a text expression showing a desire to purchase a product or a service in future. Extracting posts having PI from a user's social posts gives huge opportunities towards web personalization, targeted marketing and improving community observing systems. In this paper, we explore the novel problem of detecting PIs from social posts and classifying them. We find that using linguistic features along with statistical features of PI expressions achieves a significant improvement in PI classification over 'bag-of-words' based features used in many present day social-media classification tasks. Our approach takes into consideration the specifics of social posts like limited contextual information, incorrect grammar, language ambiguities, etc. by extracting features at two different levels of text granularity - word and phrase based features and grammatical dependency based features. Apart from these, the patterns observed in PI posts help us to identify some specific features.
Linguistic Reflections of Student Engagement in Massive Open Online Courses
Authors: Wen Miaomiao , Yang Diyi , Rose Carolyn
Published: November 2022

While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time.  Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course.  As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts.  We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered.  Theoretical and practical implications will be discussed.
Modeling User Attitude toward Controversial Topics in Online Social Media
Authors: Gao Huiji , Mahmud Jalal , Chen Jilin , Nichols Jeffrey , Zhou Michelle
Published: November 2022

The increasing use of social media platforms like Twitter has attracted a large number of online users to express their attitude toward certain topics. Sentiment, opinion, and action, as three essential aspects of user attitude, have been studied separately in various existing research work. Investigating them together not only brings unique challenges but can also help better understand a user's online behavior and benefit a set of applications related to online campaign and recommender systems. In this paper, we present a computational model that estimates individual social media user's attitude toward controversial topics in terms of the three aspects and their relationships. Our model can simultaneously capture the three aspects so as to predict action and sentiment based on one's opinions.Experiments on multiple social media campaign datasets demonstrated that our attitude model can more effectively predict people's sentiment, opinion and action than approaches that treat these aspects separately.
How Community Feedback Shapes User Behavior
Authors: Cheng Justin , Danescu-Niculescu-Mizil Cristian , Leskovec Jure
Published: November 2022

Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split.
Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media
Authors: Garcia David , Weber Ingmar , Garimella Venkata
Published: November 2022

The subjective nature of gender inequality motivates the analysis and comparison of data from real and fictional human interaction. We present a computational extension of the Bechdel test: A popular tool to assess if a movie contains a male gender bias, by looking for two female characters who discuss about something besides a man. We provide the tools to quantify Bechdel scores for both genders, and we measure them in movie scripts and large datasets of dialogues between users of MySpace and Twitter. Comparing movies and users of social media, we find that movies and Twitter conversations have a consistent male bias, which does not appear when analyzing MySpace. Furthermore, the narrative of Twitter is closer to the movies that do not pass the Bechdel test than to those that pass it. We link the properties of movies and the users that share trailers of those movies. Our analysis reveals some particularities of movies that pass the Bechdel test: Their trailers are less popular, female users are more likely to share them than male users, and users that share them tend to interact less with male users. Based on our datasets, we define gender independence measurements to analyze the gender biases of a society, as manifested through digital traces of online behavior. Using the profile information of Twitter users, we find larger gender independence for urban users in comparison to rural ones. Additionally, the asymmetry between genders is larger for parents and lower for students. Gender asymmetry varies across US states, increasing with higher average income and latitude. This points to the relation between gender inequality and social, economical, and cultural factors of a society, and how gender roles exist in both fictional narratives and public online dialogues.
Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data
Authors: Georgiev Petko , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games.In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.
Audience-Aware Credibility: From Understanding Audience to Establishing Credible Blogs
Authors: Rieh Soo Young , Jeon Grace YoungJoo , Yang Ji Yeon , Lampe Clifford
Published: November 2022

This study examines how bloggers establish and enhance the credibility of their blogs through a series of blogging practices. Based on an analysis of interviews with 22 independent bloggers who blog on a range of topics, we present audience-aware credibility as a theoretical construct. Audience-aware credibility is defined as how bloggers signal their credibility based on who they think their audience is and how they provide value to that perceived audience. The analysis of bloggers’ credibility constructs, conceptualizations of audience, and perceived blog value identified four types of bloggers who constructed audience-aware credibility in distinctive ways: Community Builder, Expertise Provider, Topic Synthesizer, and Information Filterer. We then report on these bloggers’ blogging practices for establishing credibility and strategies for interacting with their audience to enhance credibility. The contributions of this study are to expand credibility constructs for social media research and to demonstrate the role of credibility perceptions in content contributors’ online activities. The findings reveal that a multi-dimensional construct of audience-aware credibility serves as a driving factor influencing and shaping blogging practices of all four types of bloggers. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity
Authors: De Choudhury Munmun , De Sushovan
Published: November 2022

Social media is continually emerging as a platform of information exchange around health challenges. We study mental health discourse on the popular social media:reddit. Building on findings about health information seeking and sharing practices in online forums, and social media like Twitter, we address three research challenges. First, we present a characterization of self-disclosure inmental illness communities on reddit. We observe individuals discussing a variety of concerns ranging from the daily grind to specific queries about diagnosis and treatment. Second, we build a statistical model to examine the factors that drive social support on mental health reddit communities. We also develop language models to characterize mental health social support, which are observed to bear emotional, informational, instrumental, and prescriptive information. Finally, we study disinhibition in the light of the dissociative anonymity that reddit’s throwaway accounts provide. Apart from promoting open conversations,such anonymity surprisingly is found to gather feedback that is more involving and emotionally engaging. Our findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness. They also expand our understanding of the role of the social web in behavioral therapy.
Great Question! Question Quality in Community Q&A
Authors: Ravi Sujith , Pang Bo , Rastogi Vibhor , Kumar Ravi
Published: November 2022

Asking the right question in the right way is an art (and a science). In a community question-answering setting, a good question is not just one that is found to be useful by other people: a question is good if it is also presented clearly and shows prior research.  Using a community question-answering site that allows voting over the questions, we show that there is a notion of question quality that goes beyond mere popularity. We present techniques using latent topic models to automatically predict the quality of questions based on their content. Our best system achieves a prediction accuracy of 72%, beating out strong baselines by a significant amount. We also examine the effect of  question quality on the dynamics of user behavior and the longevity of questions.
Glaucus: Exploiting the Wisdom of Crowds for Location-Based Queries in Mobile Environments
Authors: Choy Minsoo , Lee Jae-Gil , Gweon Gahgene , Kim Daehoon
Published: November 2022

In this paper, we build a social search engine named Glaucus for location-based queries. They compose a significant portion of mobile searches, thus becoming more popular with the prevalence of mobile devices. However, most of existing social search engines are not designed for location-based queries and thus often produce poor-quality results for such queries. Glaucus is inherently designed to support location-based queries. It collects the check-in information, which pinpoints the places where each user visited, from location-based social networking services such as Foursquare. Then, it calculates the expertise of each user for a query by using our new probabilistic model called the location aspect model. We conducted two types of evaluation to prove the effectiveness of our engine. The results showed that Glaucus selected the users supported by stronger evidence for the required expertise than existing social search engines. In addition, the answers from the experts selected by Glaucus were highly rated by our human judges in terms of answer satisfaction.
Efficient Filtering on Hidden Document Streams
Authors: Ruiz Eduardo , Hristidis Vagelis , Ipeirotis Panagiotis
Published: November 2022

Many online services like Twitter and GNIP offer streaming programming interfaces that allow real-time information filtering based on keyword or other conditions. However, all these services specify strict access constraints, or charge a cost based on the usage. We refer to such streams as ``hidden streams'' to draw a parallel to the well-studied hidden Web, which similarly restricts access to the contents of a database through a querying interface. At the same time, the users' interest is often captured by complex classification models that, implicitly or explicitly, specify hundreds of keyword-based rules, along with the rules' accuracies. In this paper, we study how to best utilize a constrained streaming access interface to maximize the number of retrieved relevant items, with respect to a classifier, expressed as a set of rules. We consider two problem variants. The static version assumes that the popularity of the keywords is known and constant across time. The dynamic version lifts this assumption, and can be viewed as an exploration-vs.-exploitation problem. We show that both problems are NP-hard, and propose exact and bounded approximation algorithms for various settings, including various access constraint types. We experimentally evaluate our algorithms on real Twitter data.
Why Won't Aliens Talk to Us? Content and Community Dynamics in Online Citizen Science
Authors: Luczak-Roesch Markus , Tinati Ramine , Simperl Elena , Van Kleek Max , Shadbolt Nigel , Simpson Robert
Published: November 2022

We conducted a quantitative analysis of ten citizen science projects hosted on the Zooniverse platform, using a data set of over 50 million activity records and more than 250,000 users, collected between December 2010 and July 2013. We examined the level of participation of users in Zooniverse discussion forums in relation to their contributions toward the completion of scientific (micro-)tasks. As Zooniverse is home to a multitude of projects, we were also interested in the emergence of cross-projects effects, and identified those project characteristics, most importantly the subject domain and the duration of a project. We also looked into the adoption of expert terminology, showing that this phenomenon is dependent on the scientific domain which a project addresses but also affected by how the communication features are actually used by a community. This is the first study of this kind in this increasingly important class of online community, and its insights will inform the design and further development of the Zooniverse platform, and of citizen science systems as a whole.
Creating and Sharing Records of Multiplayer Online Game Play: Practices and Attitudes
Authors: Shipman Frank , Marshall Catherine
Published: November 2022

Online games offer multiple ways for players to interact. Recordings of these interactions are used for a variety of purposes. Such recordings raise ownership issues similar to those arising from the use of other online media. This paper presents an analysis of the attitudes and practices of 241 online gamers, 36% of whom reported recording gameplay and 29% of whom reported watching or accessing records of gameplay. We use a series of scenarios and hypothetical statements to elicit reactions to varying features of the production and use of these records; the parties involved, the type of records, and how they are used may all influence perceived ownership rights. Players are more sensitive to the recording and reuse of in-game textual communication than they are to recordings of avatar activity in the virtual world. Particularly negative reactions were elicited by scenarios that proposed a search capability over players’ textual communication or the reuse of such in-game communication in the software publisher’s advertisements. Additionally, players are skeptical of institutional archiving of such game content, although 50 year embargos on access brought attitudes in line with those for other media.
A Tale of Cities: Urban Biases in Volunteered Geographic Information
Authors: Hecht Brent , Stephens Monica
Published: November 2022

Geotagged tweets, Foursquare check-ins and other forms of volunteered geographic information (VGI) play a critical role in numerous studies and a large range of intelligent technologies. We show that three of the most commonly used sources of VGI – Twitter, Flickr, and Foursquare – are biased towards urban perspectives at the expense of rural ones. Utilizing a geostatistics-based approach, we demonstrate that, on a per capita basis, these important VGI datasets have more users, more information, and higher quality information within metropolitan areas than outside of them. VGI is a subset of user-generated content (UGC) and we discuss how our results suggest that urban biases might exist in non-geographically referenced UGC as well. Finally, because Foursquare is exclusively made up of VGI, we argue that Foursquare (and possibly other location-based social networks) has fundamentally failed to appeal to rural populations.
EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets
Authors: Kempter Renato , Sintsova Valentina , Musat Claudiu , Pu Pearl
Published: November 2022

Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time.  Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful analysis to be desired.  In this paper, we suggest using a fine-grained, multi-category emotion model to classify and visualize users' emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and validating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems.
Rumor Cascades
Authors: Friggeri Adrien , Adamic Lada , Eckles Dean , Cheng Justin
Published: November 2022

Online social networks provide a rich substrate for rumor propagation. Information received via friends tends to be trusted, and online social networks allow individuals to transmit information to many friends at once. By referencing known rumors from Snopes.com, a popular website documenting memes and urban legends, we track the propagation of thousands of rumors appearing on Facebook. From this sample we infer the rates at which rumors from different categories and of varying truth value are uploaded and reshared. We find that rumor cascades run deeper in the social network than reshare cascades in general. We then examine the effect of individual reshares receiving a comment containing a link to a Snopes article on the evolution of the cascade. We find that receiving such a comment increases the likelihood that a reshare of a rumor will be deleted. Furthermore, large cascades are able to accumulate hundreds of Snopes comments while continuing to propagate. Finally, using a dataset of rumors copied and pasted from one status update to another, we show that rumors change over time and that different variants tend to dominate different bursts in popularity.
Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns
Authors: Mahmud Jalal , Gao Huiji
Published: November 2022

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers’ sentiment towards the cam-paign topic and their rate of retweeting of messages gener-ated by the campaign. Our analysis with followers of mul-tiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online inter-vention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher re-tweet rate than a number of other baseline approaches.