bluuebunny/crossref_metadata_2025 huggingface dataset:
Who Should I Follow? Recommending People in Directed Social Networks
Authors: Brzozowski Michael , Romero Daniel
Published: November 2022

A variety of social networks feature a directed attention or "follower" network. In this paper, we compare several methods of recommending new people for users to follow. We analyzed structural patterns in a directed social network to evaluate the likelihood that they will predict a future connection, and use these observations to inform an intervention experiment where we offer users of this network new people to connect to. This paper compares a variety of features for recommending users and presents design implications for social networking services. Certain types of structural closures significantly outperform recommendations based on traditional collaborative filtering, behavioral, and similarity features. We find that sharing an audience with someone is a surprisingly compelling reason to follow them, and that similarity is much less persuasive. We also find evidence that organic network growth is very different from how users behave when they are prompted to connect to new people.
Mapping Information Flows on Twitter
Authors: Lotan Gilad
Published: November 2022

Social network services like Twitter and Facebook have created an expectation that you interact with your customers, followers and friends. There’s an expectation to connect rather than broadcast, listen and engage in conversations. But how can we expect to interact with our invisible audience when we can’t really see whose there? For the first time in history, there is a plethora of information produced by people’s actions. We can now observe a friend take another’s recommendation to purchase an item, or a powerful stream of clicks to content that we choose to curate. Social media professionals are jumping on the bandwagon and attempting to quantify social interactions by using terms like influence, reach, trust and klout. But even though data is more visible than ever, it is still representative of people’s complex reasoning mechanism, changing relationships, timing and logic. This paper looks at two different ways to analyze and display characteristics of online audiences on Twitter through information flows. By visualizing flows, it is possible to “put a face” to an audience, seeing interactions between interconnected users. By replaying a representation of the series of events, it is possible to note key moments in the act of information dissemination.
Frankenplace: An Application for Similarity-Based Place Search
Authors: Adams Benjamin , McKenzie Grant
Published: November 2022

When experiencing or describing a new place people will often compare it against other places that they already know. However, this human attention to the simultaneous similarities and differences between places is not reflected in the design of user interfaces of current place search technologies. In this demo, we present Frankenplace, an application for doing similarity-based place search that allows users to interactively find new places based on mixtures of features drawn from different places. The features of places are derived from a combination of authoritative data sources and unstructured observation data from social media, and organized into an extensible set of layers. We demonstrate the Frankenplace interface, which lets a user build a profile of a target place by selecting the most relevant of the properties shared by known places.
Composing Traveling Paths from Location-Based Services
Authors: Hsieh Hsun-Ping , Li Cheng-Te
Published: November 2022

With the emergence of location-based services, such as Foursquare and Gowalla, users are allowed to easily perform check-in actions anywhere and anytime. The location-based check-in not only enables personal geospatial journeys but also serves as a kind of fine-grained source for trip planning. In this work, we aim to collectively compose traveling paths by leveraging the check-in data through mining the moving behaviors of users. A novel system, TP-Comp, is developed. To compose travel paths, TP-Comp not only allows users to specify starting/end and/or must-go locations, but also provides the flexibility of the time constraint requirement (i.e., the expected duration of the trip). By considering a sequence of check-in points as a traveling path, we mine the frequent sequences with some ranking mechanism to achieve the goal. Our TP-Comp targets at travelers who are unfamiliar to the objective area/city and have time limitation in the trip.
Conference Committee
Authors: Breslin John
Published: November 2022

Chairs and committess of the 2012 ICWSM conference.
Workshops
Authors: Breslin John
Published: November 2022

THe 2012 ICWSM conference will hold two workshops: Social Media Visualization, and Real-Time Analysis and Mining of Social Streams.
On the Study of Social Interactions in Twitter
Authors: Macskassy Sofus
Published: November 2022

Twitter and other social media platforms are increasingly used as the primary way in which people speak with each other. As opposed to other platforms, Twitter is interesting in that many of these dialogues are public and so we can get a view into the dynamics of dialogues and how they differ from other other tweet behaviors. We here analyze tweets gathered from 2400 twitter streams over a one month period. We study social interactions in three important dimensions: what are the salient user behaviors in terms of how often they have social interactions and how these interactions are spread among different people; what are the characteristics of the dialogues, or sets of tweets, that we can extract from these interactions, and what are the characteristics of the social network which emerges from considering these interactions? We find that roughly half of the users spend a fair amount of time interacting whereas 40% of users do not seem to have active interactions. We also find that the vast majority of active dialogues only involve two people despite the public nature of these tweets. We finally find that while the emerging social network does contain a giant component, the component clearly is a set of well-defined tight clusters which are loosely connected.
You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress
Authors: Dinakar Karthik , Jones Birago , Lieberman Henry , Picard Rosalind , Rose Carolyn , Thoman Matthew , Reichart Roi
Published: November 2022

Adolescent cyber-bullying on social networks is a phenomenon that has received widespread attention. Recent work by sociologists has examined this phenomenon under the larger context of teenage drama and it's manifestations on social networks. Tackling cyber-bullying involves two key components – automatic detection of possible cases, and interaction strategies that encourage reflection and emotional support. Key is showing distressed teenagers that they are not alone in their plight. Conventional topic spotting and document classification into labels like "dating" or "sports" are not enough to effectively match stories for this task. In this work, we examine a corpus of 5500 stories from distressed teenagers from a major youth social network. We combine Latent Dirichlet Allocation and human interpretation of its output using principles from sociolinguistics to extract high-level themes in the stories and use them to match new stories to similar ones. A user evaluation of the story matching shows that theme-based retrieval does a better job of finding relevant and effective stories for this application than conventional approaches.
On the Study of Diurnal Urban Routines on Twitter
Authors: Naaman Mor , Zhang Amy , Brody Samuel , Lotan Gilad
Published: November 2022

Social media activity in different geographic regions can expose a varied set of temporal patterns. We study and characterize diurnal patterns in social media data for different urban areas, with the goal of providing context and framing for reasoning about such patterns at different scales. Using one of the largest datasets to date of Twitter content associated with different locations, we examine within-day variability and across-day variability of diurnal keyword patterns for different locations. We show that only a few cities currently provide the magnitude of content needed to support such across-day variability analysis for more than a few keywords. Nevertheless, within-day diurnal variability can help in comparing activities and finding similarities between cities.
Grief-Stricken in a Crowd: The Language of Bereavement and Distress in Social Media
Authors: Brubaker Jed , Kivran-Swaine Funda , Taber Lee , Hayes Gillian
Published: November 2022

People turn to social media to express their emotions surrounding major life events. Death of a loved one is one scenario in which people share their feelings in the semi-public space of social networking sites. In this paper, we present the results of a two-part investigation of grief and distress in the context of messages posted to the profiles of deceased MySpace users. We present coding system for identifying emotion distressed content, followed by a detailed analysis of language use that lays a foundation for natural language processing (NLP) tasks, such as automatic detection of bereavement-related distress. Our findings suggest that in addition to words bearing positive or negative sentiment, linguistic style can be an indicator of messages that demonstrate distress in the space of post-mortem social media content. These results contribute to research in computational linguistics by identifying linguistic features that can be used for automatic classification as well as to research on death and bereavement by enumerating attributes of distressed self-expression in a post-mortem context.
OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates
Authors: Kıcıman Emre
Published: November 2022

Many studies have shown that social data such as tweets are a rich source of information about the real-world including, for example, insights into health trends. A key limitation when analyzing Twitter data, however, is that it depends on people self-reporting their own behaviors and observations. In this paper, we present a large-scale quantitative analysis of some of the factors that influence self-reporting bias. In our study, we compare a year of tweets about weather events to ground-truth knowledge about actual weather occurrences. For each weather event we calculate how extreme, how expected, and how big a change the event represents. We calculate the extent to which these factors can explain the daily variations in tweet rates about weather events. We find that we can build global models that take into account basic weather information, together with extremeness, expectation and change calculations to account for over 40% of the variability in tweet rates. We build location-specific (i.e., a model per each metropolitan area) models that account for an average of 70% of the variability in tweet rates.
Who Does What on the Web: A Large-Scale Study of Browsing Behavior
Authors: Goel Sharad , Hofman Jake , Sirer M.
Published: November 2022

As the Web has become integrated into daily life, understanding how individuals spend their time online impacts domains ranging from public policy to marketing. It is difficult, however, to measure even simple aspects of browsing behavior via conventional methods---including surveys and site-level analytics---due to limitations of scale and scope. In part addressing these limitations, large-scale Web panel data are a relatively novel means for investigating patterns of Internet usage. In one of the largest studies of browsing behavior to date, we pair Web histories for 250,000 anonymized individuals with user-level demographics---including age, sex, race, education, and income---to investigate three topics. First, we examine how behavior changes as individuals spend more time online, showing that the heaviest users devote nearly twice as much of their time to social media relative to typical individuals. Second, we revisit the digital divide, finding that the frequency with which individuals turn to the Web for research, news, and healthcare is strongly related to educational background, but not as closely tied to gender and ethnicity. Finally, we demonstrate that browsing histories are a strong signal for inferring user attributes, including ethnicity and household income, a result that may be leveraged to improve ad targeting.
The Length of Bridge Ties: Structural and Geographic Properties of Online Social Interactions
Authors: Volkovich Yana , Scellato Salvatore , Laniado David , Mascolo Cecilia , Kaltenbrunner Andreas
Published: November 2022

The popularity of the Web has allowed individuals to communicate and interact with each other on a global scale: people connect both to close friends and acquaintances, creating ties that can bridge otherwise separated groups of people. Recent evidence suggests that spatial distance is still affecting social links established on online platforms, with online ties preferentially connecting closer people. In this work we study the relationships between interaction strength, spatial distance and structural position of ties between members of a large-scale online social networking platform, Tuenti. We discover that ties in highly connected social groups tend to span shorter distances than connections bridging together otherwise separated portions of the network. We also find that such bridging connections have lower social interaction levels than ties within the inner core of the network and ties connecting to its periphery. Our results suggest that spatial constraints on online social networks are intimately connected to structural network properties, with important consequences for information diffusion.
Around the Water Cooler: Shared Discussion Topics and Contact Closeness in Social Search
Authors: Komanduri Saranga , Fang Lujun , Huffaker David , Staddon Jessica
Published: November 2022

Search engines are now augmenting search results with social annotations, i.e., endorsements from users’ social network contacts. However, there is currently a dearth of published research on the effects of these annotations on user choice. This work investigates two research questions associated with annotations: 1) do some contacts affect user choice more than others, and 2) are annotations relevant across various information needs. We conduct a controlled experiment with 355 participants, using hypothetical searches and annotations, and elicit users’ choices. We find that domain contacts are preferred to close contacts, and this preference persists across a variety of information needs. Further, these contacts need not be experts and might be identified easily from conversation data.
Analysis of Elderly Persons’ Social Network: Need for an Appropriate Online Platform
Authors: Fritsch Tobias , Steinke Frederick , Brem Daniel
Published: November 2022

The increasing demographic change and the upcoming technology affinity of the elderly population provide many Internet-based opportunities for social interconnection. Thus, we focus on the status quo of social contacts, the use of the Internet as well as online information offerings and support in the neighborhood for the target group (age of 60 to 90). Hence, two partial standardized surveys including 37 and 50 participants with average ages of 68 and 71 years were conducted respectively. Results indicate that social contacts decrease with age and 75 per cent of the seniors already use the Internet. Thus, there is a general high importance of several web-based information offerings.
Opinion Retrieval in Twitter
Authors: Luo Zhunchen , Osborne Miles , Wang Ting
Published: November 2022

We consider the problem of finding opinionated tweets about a given topic. We automatically construct opinionated lexica from sets of tweets matching specific patterns indicative of opinionated messages. When incorporated into a learning-to-rank approach, results show that this automatically opinionated information yields retrieval performance comparable with a manual method. Finally, topic-related specific structured tweet sets can help improve query-dependent opinion retrieval.
Where Online Friends Meet: Social Communities in Location-Based Networks
Authors: Brown Chloë , Nicosia Vincenzo , Scellato Salvatore , Noulas Anastasios , Mascolo Cecilia
Published: November 2022

Recent research suggests that, as in offline scenarios, spatial proximity increases the likelihood that two individuals establish an online social connection, and geographic closeness could therefore influence the formation of online communities. In this work we present a study of communities in two online social networks with location-sharing features and analyze their social and spatial properties. We study the places users visit to understand whether communities revolve around places or whether they exist independently. Our results suggest that community structure in social networks may arise from both social and spatial factors, so that exploiting information about the places where people go could benefit the definition of new community detection methods and community evolution models.
Identifying Microblogs for Targeted Contextual Advertising
Authors: Dave Kushal , Varma Vasudeva
Published: November 2022

Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a two-step classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog. The systems use features derived from the Part-of-speech tags, the tweet content and uses external resources such as query logs and n-gram dictionaries from previously labeled data.This work aims at providing a thorough insight into the problem and analyzing various features to assess which features contribute the most towards identifying the tweets that can be targeted for advertisements.
On the Geo-Indicativeness of Non-Georeferenced Text
Authors: Adams Benjamin , Janowicz Krzysztof
Published: November 2022

Geographic location is a key component for information retrieval on the Web, recommendation systems in mobile computing and social networks, and place-based integration on the Linked Data cloud. Previous work has addressed how to estimate locations by named entity recognition, from images, and via structured data. In this paper, we estimate geographic regions from unstructured, non geo-referenced text by computing a probability distribution over the Earth's surface. Our methodology combines natural language processing, geostatistics, and a data-driven bottom-up semantics. We illustrate its potential for mapping geographic regions from non geo-referenced text.
Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations
Authors: Kim Suin , Bak JinYeong , Oh Alice
Published: November 2022

We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus.
Evolutionary Clustering and Analysis of User Behaviour in Online Forums
Authors: Morrison Donn , McLoughlin Ian , Hogan Alice , Hayes Conor
Published: November 2022

In this paper we cluster and analyse temporal user behaviour in online communities. We adapt a simple unsupervised clustering algorithm to an evolutionary setting where we cluster users into prototypical behavioural roles based on features derived from their ego-centric reply-graphs. We then analyse changes in the role membership of the users over time, the change in role composition of forums over time and examine the differences between forums in terms of role composition. We perform this analysis on 200 forums from a popular national bulletin board and 14 enterprise technical support forums.
Understanding Actionable Knowledge in Social Media: BBC Question Time and Twitter, a Case Study
Authors: Ferrario Maria Angela , Simm Will , Whittle Jon , Rayson Paul , Terzi Maria , Binner Jane
Published: November 2022

Automatically extracting actionable knowledge from on-line social media has attracted a growing interest from both academia and the industry. However, little clarity exists in relation to what actionable knowledge is, whether it can be measured and where it is more likely to be found. This paper makes an attempt at answering the above questions by gaining a better understanding of actionable knowledge in Twitter.
Why You Are More Engaged: Factors Influencing Twitter Engagement in Occupy Wall Street
Authors: Chen Jilin , Pirolli Peter
Published: November 2022

Twitter has been used for engaging with audiences online in several popular political movements. In this paper we explore factors that influence the engagement of Twitter users during the recent Occupy Wall Street movement, where engagement is measured by retweets and hashtag usage related to the movement. Through analyzing Twitter activities of more than 18,000 users, we found that users’ general activity level, geographic location, topic interests and interpersonal interactions before the movement all had measurable effects on users’ engagement level during the movement.
Computational Predictors in Online Social Deliberations
Authors: Woolf Beverly , Murray Thomas , Xu Xiaoxi , Osterweil Leon , Clarke Lori , Wing Leah , Katsh Ethan
Published: November 2022

This research seeks to identify online participants’ disposition and skills. A prototype dashboard and annotation scheme were developed to support facilitators and several computational predictors were identified that show statistically significant correlations with dialogue skills as observed by human annotators.
Visualizing Media Bias through Twitter
Authors: An Jisun , Cha Meeyoung , Gummadi Krishna , Crowcroft Jon , Quercia Daniele
Published: November 2022

Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Therefore, tracking bias in everyday news and building a platform where people can receive balanced news information is important. We propose a model that maps the news media sources along a dimensional dichotomous political spectrum using the co-subscriptions relationships inferred by Twitter links. By analyzing 7 million follow links, we show that the political dichotomy naturally arises on Twitter when we only consider direct media subscription. Furthermore, we demonstrate a real-time Twitter-based application that visualizes an ideological map of various media sources.
Trendminer: An Architecture for Real Time Analysis of Social Media Text
Authors: Preotiuc-Pietro Daniel , Samangooei Sina , Cohn Trevor , Gibbins Nicholas , Niranjan Mahesan
Published: November 2022

The emergence of online social networks (OSNs) and the accompanying availability of large amounts of data, pose a number of new natural language processing (NLP) and computational challenges. Data from OSNs is different to data from traditional sources (e.g. newswire). The texts are short, noisy and conversational. Another important issue is that data occurs in a real-time streams, needing immediate analysis that is grounded in time and context. In this paper we describe a new open-source framework for efficient text processing of streaming OSN data (available at www.trendminer-project.eu). Whilst researchers have made progress in adapting or creating text analysis tools for OSN data, a system to unify these tasks has yet to be built. Our system is focused on a real world scenario where fast processing and accuracy is paramount. We use the MapReduce framework for distributed computing and present running times for our system in order to show that scaling to online scenarios is feasible.We describe the components of the system and evaluate their accuracy. Our system supports easy integration of future modules in order to extend its functionality.
A Systematic Investigation of Blocking Strategies for Real-Time Classification of Social Media Content into Events
Authors: Reuter Timo , Cimiano Philipp
Published: November 2022

Events play a prominent role in our lives, such that many social media documents describe or are related to some event. Organizing social media documents with respect to events thus seems a promising approach to better manage and organize the ever-increasing amount of user-generated content in social media applications. It would support the navigation of data by events or allow one to get notified about new postings related to the events one is interested in, just to name two applications. A challenge is to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. We present a system that is able to classify a stream of social media data into a growing and evolving set of events. In order to scale up to the data sizes and data rates in social media applications, the use of a candidate retrieval or blocking step is crucial to reduce the number of events that are considered as potential candidates to which the incoming data point could belong to.In this paper we present and experimentally compare different blocking strategies along their cost vs. effectiveness tradeoff.We show that using a blocking strategy that selects the 60 closest events with respect to upload time, we reach F-Measures of about 85.1% while being able to process the incoming documents within 32ms on average. We thus provide a principled approach supporting to scale up classification of social media documents into events and to process the incoming stream of documents in real time.
Preface
Authors: Archambault Daniel
Published: November 2022

The Social Media Visualization workshop is a venue for the presentation of research and applications of visualization to social media data. Its goal is to bring together researchers and industry practitioners interested in visual and interactive techniques for social media analysis, particularly in social sciences and humanities as well as in industry.
Mapping Community Engagement with Urban Crowd-Sourcing
Authors: Hristova Desislava , Mashhadi Afra , Quattrone Giovanni , Capra Licia
Published: November 2022

Cities are highly dynamic entities, with urban elements such as businesses, cultural and social Points-of- Interests (POIs), housing, transportation and the like, continuously changing. In order to maintain accurate spatial information in these settings, crowd-sourcing models of data collection, such as in OpenStreetMap (OSM), have come under investigation. Like many crowd-sourcing platforms (e.g., Wikipedia), these geo- wikis exhibit tailing-off activity, bringing into ques- tion their long-term viability. In this paper, we begin an investigation into the sustainability of urban crowd-sourcing, by studying the network structure and ge- ographical mapping of implicit communities of con- tributors in OSM. We observe that spatially clustered crowd-sourcing communities produce higher coverage than those with looser geographic affinity. We discuss the positive implications that this has on the future of urban crowd-sourcing.
Feasibility Study on Detection of Transportation Information Exploiting Twitter as a Sensor
Authors: Sasaki Kenta , Nagano Shinichi , Ueno Koji , Cho Kenta
Published: November 2022

The concept of a smart community has recently been attracting great attention as a means of utilizing energy effectively. One of the modules constituting the smart community is an intelligent transportation system, in which various sensors track movements of people and vehicles in real time to optimize migration pathways or means. Social media have the potential to serve as sensors, since people often post transportation information on such media. This paper presents a feasibility study on detecting information, focusing on train status information, by exploiting Twitter as a sensor. We dealt with two issues: (1) for the ambiguity of textual information expressed in tweets, we utilized heuristic rules in text manipulation, and (2) for the differences in the numbers of tweets among train lines, we optimized parameter values in statistical analysis for each train line. The experimental results show that the F-measure of detecting the information was more than 0.85 and the time taken to detect the information was less than 4 minutes. As a result we confirmed the high potential of detecting transportation information through Twitter.
OurCity: Understanding How Visualization and Aggregation of User-Generated Content Can Engage Citizens in Community Participation
Authors: Simm Will , Whittle Jon , Nieman Adam , Portman Anna , Sibbald John
Published: November 2022

OurCity is a site-specific digital artwork designed to solicit, aggregate and visualize citizens’ views on the cities in which they live. It aims to allow people to have their voice heard in a way which is fun and engaging and reduces the gap between citizens and policymakers. OurCity builds on our previous work, VoiceYourView (Whittle et al 2010) which used similar data aggregation techniques but a completely different visualization of user-generated data. This paper revisits the key results from VoiceYourView and hence uses OurCity as an additional validation exercise to assess whether VoiceYourView results are generalizable.
Getting Acquainted with Groups and Individuals: Information Seeking, Social Uncertainty and Social Network Sites
Authors: Doodson James , Gavin Jeff , Joiner Richard
Published: November 2022

This study examined whether the relationship between information-seeking and social uncertainty differed when information was sought about a specific individual (e.g. a new housemate) or a group (e.g. a group of new housemates). An online experiment recruited 488 first-year undergraduates in the weeks immediately before starting a new university. Four information-seeking strategies (Ramirez, Walther, Burgoon & Sunnafrank, 2002) successfully modeled how students sought information about each other using Social Network Sites. Whereas an interactive strategy predicted lower social uncertainty about individuals than groups, a passive strategy predicted higher social uncertainty for indi- viduals and lower social uncertainty for groups. Findings are discussed in the context of impression formation, specif- ically Entitativity (Hamilton & Sherman, 1995).
Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes
Authors: Bauckhage Christian , Kersting Kristian , Hadiji Fabian
Published: November 2022

Internet memes are a pervasive phenomenon on the social Web. They typically consist of viral catch phrases, images, or videos that spread through instant messaging, (micro) blogs, forums, and social networking sites. Due to their popularity and proliferation, Internet memes attract interest in areas as diverse as marketing, sociology, or computer science and have been dubbed a new form of communication or artistic expression. In this paper, we examine the merits of such claims and analyze how collective attention into Internet memes evolves over time. We introduce and discuss statistical models of the dynamics of fads and fit them to meme related time series obtained from Google Trends. Given data as to more than 200 memes, we find that our models provide more accurate descriptions of the dynamics of growth and decline of collective attention to individual Internet memes than previous approaches from the literature. In short, our results suggest that Internet memes are nothing but fads.
Detecting Friendship Within Dynamic Online Interaction Networks
Authors: Merritt Sears , Jacobs Abigail , Mason Winter , Clauset Aaron
Published: November 2022

In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.
Feature Sentiment Diversification of User Generated Reviews: The FREuD Approach
Authors: Naveed Nasir , Gottron Thomas , Staab Steffen
Published: November 2022

Online discussions, user reviews and comments onthe Social Web are valuable sources of informationabout products, services, or shared contents. The rapidlygrowing popularity and activity of Web communitiesraises novel questions of appropriate aggregation anddiversification of such social contents. In many cases,users are interested in gaining an extensive overviewover pros and cons of a particular track of contributions.We address the problem of social content diversificationby combining latent semantic analysis with featurecentricsentiment analysis. Our FREuD approach providesa representative overview of sub-topics and aspectsof discussions, characteristic user sentiments underdifferent aspects, and reasons expressed by differentopponents. In experiments with real world productreviews we compare FREuD to the typical implementationof ranking reviews by the usefulness rating providedby users as well as a naive sentiment diversificationalgorithms based on star ratings. To this end we hadhuman users provide a fine-grained gold standard aboutthe coverage of features and sentiments in reviews forseveral products in three categories. We observed thatFREuD clearly outperforms the baseline algorithms ingenerating a sentiment-diversified set of user reviewsfor a given product.
Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects
Authors: Momeni Elaheh , Cardie Claire , Ott Myle
Published: November 2022

User-generated comments in online social media have recently been gaining increasing attention as a viable source of general-purpose descriptive annotations for digital objects like photos or videos. Because users have different levels of expertise, however, the quality of their comments can vary from very useful to entirely useless. Our aim is to provide automated support for the curation of useful user-generated comments from public collections of digital objects. After constructing a crowd-sourced gold standard of useful and not useful comments, we use standard machine learning methods to develop a usefulness classifier, exploring the impact of surface-level, syntactic, semantic, and topic-based features in addition to extra-linguistic attributes of the author and his or her social media activity. We then adapt an existing model of prevalence detection that uses the learned classifier to investigate patterns in the commenting culture of two popular social media platforms. We find that the prevalence of useful comments is platform-specific and is further influenced by the entity type of the media object being commented on (person, place, event), its time period (e.g., year of an event), and the degree of polarization among commenters.
Fitter with Twitter: Understanding Personal Health and Fitness Activity in Social Media
Authors: Teodoro Rannie , Naaman Mor
Published: November 2022

With the growing interest in how online sedentary activity can mediate offline health practices, we present a study of social media activity related to personal health and fitness. We aim to identify the type of content and motivations for sharing health-related activity in social media outlets. To this end, we performed a qualitative analysis of Twitter posts, as well as an extensive set of interviews with experienced users who post messages on Twitter about exercise, diet, and weight loss activities. The qualitative analysis exposes varying levels of activity actualization and message sentiment. The interviews help us reason about the users practices and motivations for posting activity related to the pursuit and maintenance of volitional health behaviors. Our findings extend existing theoretical frameworks and can inform the design of technology that uses social media to help initiate and maintain challenging activities like exercise and diet.
Wedding Dresses and Wanted Criminals: Pinterest.com as an Infrastructure for Repository Building
Authors: Zarro Michael , Hall Catherine , Forte Andrea
Published: November 2022

We present findings from a qualitative study of activity on Pinterest.com, in which we investigated professional and personal uses of the site using interview data and observations of online activity. We find that Pinterest serves as an infrastructure for repository building that supports a wide range of activities including: discovery, collecting, collaborating, and publishing. We discuss these concepts using the language of “boundary objects” from the sociology of science. We suggest that scale is a critical dimension of boundary objects for understanding how people make sense of Pinterest and their diverse goals for using it. Profession- als often attempt to use Pinterest to create repositories that scale to groups, organizations and societies and interface with multiple social worlds whereas personal repositories often have highly localized meanings. Our approach builds on quantitative descriptions of Pinterest to understand how the site fits into a growing ecology of social network sites.
Cultural Dimensions in Twitter: Time, Individualism and Power
Authors: Garcia-Gavilanes Ruth , Quercia Daniele , Jaimes Alejandro
Published: November 2022

Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure.
Transient News Crowds in Social Media
Authors: Lehmann Janette , Castillo Carlos , Lalmas Mounia , Zuckerman Ethan
Published: November 2022

Users increasingly inform themselves of the latest news through online news services. This is further accentuated by the increasingly seamless integration of social network platforms such as Twitter and Facebook into news websites, allowing easy content sharing and distribution. This makes online social network platforms of particular interest to news providers. For instance, online news producers use Twitter to disseminate articles published on their website, to assess the popularity of their contents, but also as an information source to be used on itself. In this paper, we focus on Twitter as a medium to help journalists and news editors rapidly detect follow-up stories to the articles they publish. We propose to do so by leveraging “transient news crowds”, which are loosely-coupled groups that appear in Twitter around a particular news item, and where transient here reflects the fleeting nature of news. We define transient news crowds on Twitter, study their characteristics, and investigate how their characteristics can be used to discover related news. We validate our approach using Twitter data around news stories published by the BBC and Al Jazeera.
Leveraging Noisy Lists for Social Feed Ranking
Authors: Burgess Matthew , Mazzia Alessandra , Adar Eytan , Cafarella Michael
Published: November 2022

Active users of social networks are subjected to extreme information overload, as they tend to follow hundreds (or even thousands of other users). Aggregated social feeds on sites like Twitter are insufficient, showing superfluous content and not allowing users to separate their topics of interest or place a priority on the content being pushed to them by their “friends.” The major social network platforms have begun to implement various features to help users organize their feeds, but these solutions require significant human effort to function properly. In practice, the burden is so high that most users do not adopt these features. We propose a system that seeks to help users find more relevant content on their feeds, but does not require explicit user input. Our system, BUTTERWORTH, automatically generates a set of “rankers” by identifying sub-communities of the user’s social network and the common content they produce. These rankers are presented using human-readable keywords and allow users to rank their feed by specific topics. We achieve an average top-10 precision of 78%, as compared to a baseline of 45%, for automatically generated topics.
Modeling Youth Civic Engagement in a New World of Networked Publics
Authors: Farnham Shelly , Keyes David , Yuki Vicky , Tugwell Chris
Published: November 2022

In an increasingly networked public, the Internet and social media provide rich opportunities for reconnecting youth with their civic life. In a questionnaire study of 578 14-20 year old youth, we explore the relationship between Internet technology experiences, civic efficacy, community identification, and civic engagement in their everyday lives. Contrary to prevailing stereotypes of digital youth, we found that most rely on email and text messaging to communicate with others in their local communities about civic issues. Further, those more experienced with technologies in the public sphere (such as blogs, wikis, and Twitter) had higher levels of civic engagement. Teens who strongly identified with their local community and who had higher levels of civic efficacy were especially likely to be civically engaged. These results highlight the importance of encouraging youth to emotionally connect to their local communities, and to do so online in the public sphere rather than through more personal communication channels.
From Foursquare to My Square: Learning Check-in Behavior from Multiple Sources
Authors: Malmi Eric , Do Trinh Minh Tri , Gatica-Perez Daniel
Published: November 2022

Location-based services often use only a single mobility data source, which typically will be scarce for any new user when the system starts out. We propose a transfer learning method to characterize the temporal distribution of places of individuals by using an external, additional, large-scale check-in data set such as Foursquare data. The method is applied to the next place prediction problem, and we show that the incorporation of additional data through the proposed method improves the prediction accuracy when there is a limited amount of prior data.
A Penny for Your Tweets: Campaign Contributions and Capitol Hill Microblogs
Authors: Yano Tae , Yogatama Dani , Smith Noah
Published: November 2022

Who influences a politician’s public statements? In this paper, we explore one plausible explanation: that financial incentives from campaign contributors affect what politicians say. Based on this idea, we design text-driven models  for campaign contribution profile prediction. Using a large corpus of public microblog messages by members of the U.S. Congress, we find evidence for such an association, at the level of contributing industries. We find complementary strengths in a simple model (which has better predictive accuracy) and a more complex model (which gives a more intuitive, human-interpretable explanation).
A Model of Collaboration-based Reputation for the Social Web
Authors: McNally Kevin , O'Mahony Michael , Smyth Barry
Published: November 2022

In this paper we describe a generic approach to modeling user reputation in online social platforms based on an underlying model of collaboration. This distinguishes our approach from more conventional reputation models which are often based around ad-hoc activity metrics. We evaluate our model with respect to a conventional reputation model used by three social Q&A websites, each based on a different topical domain.
Recognising Personality Traits Using Facebook Status Updates
Authors: Farnadi Golnoosh , Zoghbi Susana , Moens Marie-Francine , De Cock Martine
Published: November 2022

Gaining insight in a web user's personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user's personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.
Organizers
Authors: Shami N.
Published: November 2022

List of organizers of the AAAI ICWSM Workshop on Social Computing for Workforce 2.0.
"Our Grief is Unspeakable'': Automatically Measuring the Community Impact of a Tragedy
Authors: Glasgow Kimberly , Fink Clayton , Boyd-Graber Jordan
Published: November 2022

Social media offer a real-time, unfiltered view of how disasters affect communities. Crisis response, disaster mental health, and — more broadly — public health can benefit from automated analysis of the public’s mental state as exhibited on social media. Our focus is on Twitter data from a community that lost members in a mass shooting and another community—geographically removed from the shooting — that was indirectly exposed. We show that a common approach for understanding emotional response in text: Linguistic Inquiry and Word Count (LIWC) can be substantially improved using machine learning. Starting with tweets flagged by LIWC as containing content related to the issue of death, we devise a categorization scheme for death-related tweets to induce automatic text classification of such content. This improved methodology reveals striking differences in the magnitude and duration of increases in death-related talk between these communities. It also detects subtle shifts in the nature of death-related talk. Our results offer lessons for gauging public response and for developing interventions in the wake of a tragedy.