bluuebunny/crossref_metadata_2025 huggingface dataset:
Event Summarization Using Tweets
Authors: Chakrabarti Deepayan , Punera Kunal
Published: November 2022

Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about "events," and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. In this paper we argue that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets. We formalize the problem of summarizing event-tweets and give a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models. In addition, through extensive experiments on real-world data we show that our model significantly outperforms some intuitive and competitive baselines.
Dimensions of Self-Expression in Facebook Status Updates
Authors: Kramer Adam , Chung Cindy
Published: November 2022

We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM). First, we examined dimensions of self-expression in all status updates from a sample of four million Facebook users from four English-speaking countries (the United States, Canada, the United Kingdom, and Australia) in order to examine how these countries vary in their self-expressions. All four countries showed a basic three-component structure, indicating that the medium is a stronger influence than country characteristics or demographics on how people use Facebook status updates. In each country, people vary in terms of the extent to which they use Informal Speech, share Positive Events, and discuss School in their Facebook status updates. Together, these factors tell us how users differ in their self-expression, and thus illustrate meaningful use cases for the product: Talking about what’s going on tends to be positive, and people vary in terms of the extent to which their status updates are short, slangy emotional expressions and topics regarding school. The specific words that define these factors showed subtle differences across countries: The use of profanity indicates fewer school words (but only in Australia), whereas the UK shows greater use of slang terms (rather than profanity) when speaking informally. The MEM also identified English-language dialects as a meaningful dimension along which the countries varied. In sum, beyond simply indicating topicality of posts, this study provides insight into how status updates are used for self-expression. We discuss several theoretical frameworks that could produce these results, and more broadly discuss the generation of theoretical frameworks from wholly empirical data (such as naturalistic Internet speech) using the MEM.
Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites
Authors: Shi Yue , Serdyukov Pavel , Hanjalic Alan , Larson Martha
Published: November 2022

Geotagged photos of users on social media sites provide abundant location-based data, which can be exploited for various location-based services, such as travel recommendation. In this paper, we propose a novel approach to a new application, i.e., personalized landmark recommendation based on users’ geotagged photos. We formulate the landmark recommendation task as a collaborative filtering problem, for which we propose a category-regularized matrix factorization approach that integrates both user-landmark preference and category-based landmark similarity. We collected geotagged photos from Flickr and landmark categories from Wikipedia for our experiments. Our experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole.
Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors
Authors: Shamma David , Yew Jude , Kennedy Lyndon , Churchill Elizabeth
Published: November 2022

In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video's view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools.
Exploring Feature Definition and Selection for Sentiment Classifiers
Authors: Mejova Yelena , Srinivasan Padmini
Published: November 2022

In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enriched features, n-grams or phrases. We then move onto more complex aspects including feature selection using frequency-based vocabulary trimming, part-of-speech and lexicon selection (three types of lexicons), as well as using expected Mutual Information (MI). Using three product and movie review datasets of various sizes, we show, for example, that some techniques are more beneficial for larger datasets than the smaller. A classifier trained on only few features ranked high by MI outperformed one trained on all features in large datasets, yet in small dataset this did not prove to be true. Finally, we perform a space and computation cost analysis to further understand the merits of various feature types.
An Empirical Study of Geographic User Activity Patterns in Foursquare
Authors: Noulas Anastasios , Scellato Salvatore , Mascolo Cecilia , Pontil Massimiliano
Published: November 2022

We present a large-scale study of user behavior in Foursquare, conducted on a dataset of about 700 thousand users that spans a period of more than 100 days. We analyze user checkin dynamics, demonstrating how it reveals meaningful spatio-temporal patterns and offers the opportunity to study both user mobility and urban spaces. Our aim is to inform on how scientific researchers could utilise data generated in Location-based Social Networks to attain a deeper understanding of human mobility and how developers may take advantage of such systems to enhance applications such as recommender systems.
Analyzing Political Trends in the Blogosphere
Authors: Demartini Gianluca , Siersdorfer Stefan , Chelaru Sergiu , Nejdl Wolfgang
Published: November 2022

In the last years, the blogosphere has become a vital part of the web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. Tracking the public opinion is usually done by conducting surveys resulting in significant costs both for interviewers and persons consulted. In this paper, we propose a method for extracting political trends in the blogosphere.To this end, we apply sentiment and time series analysis techniques in combination with aggregation methods on blog data to estimate the temporal development of opinions on politicians.
Future Link Prediction in the Blogosphere for Recommendation
Authors: Wu Shanchan , Raschid Louiqa , Rand William
Published: November 2022

The phenomenal growth in both scale and importance of social media such as blogs, micro-blogs and user-generated content, has created a need for tools that monitor information diffusion and make recommendations within these platforms. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. There are two types of links within the blogosphere; one from blog post to blog post, and another from blog post to blog channel (an event stream of blog posts). These links can be viewed as a proxy for the flow of information between blog channels and to reflect influence. Given this assumption about links, the ability to predict future links can facilitate the monitoring of information diffusion, making recommendations, and word-of-mouth (WOM) marketing. We propose different methods for link predictions and we evaluate these methods on an extensive blog dataset.
Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure
Authors: Pfeiffer Joseph , Neville Jennifer
Published: November 2022

Much of the past work in network analysis has focused on analyzing discrete graphs, where binary edges represent the “presence” or “absence” of a relationship. Since traditional network measures (e.g., betweenness centrality) assume a discrete link structure, data about complex systems must be transformed to this representation before calculating network properties. In many domains where there may be uncertainty about the relationship structure, this transformation to a discrete representation will result in a lose of information. In order to represent and reason with network uncertainty, we move beyond the discrete graph framework and develop social network measures based on a probabilistic graph representation. More specifically, we develop measures of path length, betweenness centrality, and clustering coefficient— one set based on sampling and one based on probabilistic paths. We evaluate our methods on two real-world networks, Enron and Facebook, showing that our proposed methods more accurately capture salient effects without being susceptible to local noise.
Structuring E-Brainstorming to Better Support Innovation Processes
Authors: Lorenzo Lorea , Lizarralde Osane , Santos Igor , Passant Alexandre
Published: November 2022

Innovation is a key instrument to start a transformational process based on collaboration. It is fundamental for organisations and institutions to have well defined strategies. In this context, brainstorming sessions - and e-brainstorming tools - are effective techniques to put together and associate draft ideas. Yet, in many cases, those ideas and associations do not leave enough digital footprints, are no further used or are lost. This paper introduces the use of Social and Semantic Web technologies to support e-brainstorming. In particular, we present a lightweight ontology to structure e-brainstorming sessions, and the enrichment of existing e-brainstorming tools to do so.
How Can You Not Be on Facebook?! A Digital Etnographic Exploratory Study of Portuguese Users
Authors: Cordeiro Paula
Published: November 2022

Regardless of age, people are living their lives through social networks with Facebook leading the way: how much can one know about someone else's life using Facebook pages, that is, one's daily activities, thoughts and ideas, through the activity of sharing content and status updates? The main objective of this exploratory study is to find mass mingling trends among a sample of Portuguese adult Facebook users (N=18) and further, to find how much can one know about one's life using this social network, to conclude that despite stereotypes of online creating isolation, people are living large parts of their lives online through social networks using also mobile Internet access to socialize or simply stay in touch.
Invited Talks
Authors: Breslin John
Published: November 2022

The ICWSM 2012 conference will feature invited talks by Andrew Tomkins, Lada Adamic, and Robin Dunbar.
Exploring Social-Historical Ties on Location-Based Social Networks
Authors: Gao Huiji , Tang Jiliang , Liu Huan
Published: November 2022

Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.
Grassroots Professional Development: How Teachers Use Twitter
Authors: Forte Andrea , Humphreys Melissa , Park Thomas
Published: November 2022

In an exploratory study, we used survey, interviews and content analysis techniques to understand how educators appropriate Twitter and other social media in their practice. We report on teachers’ use of Twitter, structural features of their on and offline professional networks, and the institutional policies that shape their appropriation of social media for professional use. Most importantly, our analysis suggests teachers on Twitter tend to be eager adopters of technologies and well positioned to broker information as bridges between members of their local communities of practice and other networks of educators. Based on these findings, we discuss teachers on Twitter as participants in grassroots professional development efforts and the potential for them to be powerful fomenters and enactors of reform in educational communities.
Modeling Diffusion in Social Networks Using Network Properties
Authors: Luu Duc , Lim Ee-Peng , Hoang Tuan-Anh , Chua Freddy
Published: November 2022

Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. In this paper, we establish a general probabilistic framework, which can be used to derive macro-level diffusion models, including the well known Bass Model (BM). Using this framework, we develop several other models considering the social network’s degree distribution coupled with the assumption of linear influence by neighboring adopters in the diffusion process. Through some evaluation on synthetic data, this paper shows that degree distribution actually changes during the diffusion process. We therefore introduce a multi-stage diffusion model to cope with variable degree distribution. By conducting experiments on both synthetic and real datasets, we show that our proposed diffusion models can recover the diffusion parameters from the observed diffusion data, which allows us to model diffusion with high accuracy.
Tracking Sentiment and Topic Dynamics from Social Media
Authors: He Yulan , Lin Chenghua , Gao Wei , Wong Kam-Fai
Published: November 2022

We propose a dynamic joint sentiment-topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.
What Kobe Bryant and Britney Spears Have in Common: Mining Wikipedia for Characteristics of Notable Individuals
Authors: Ng Pauline
Published: November 2022

This paper proposes a statistical methodology for mining Wikipedia to discover characteristics associated with life outcomes. The methodology is demonstrated using first names and childhood environment. By comparing over 35,000 Wikipedia biographies against spatially and temporally matched census data, we show that individuals with rare names are twice as likely to appear in Wikipedia (RR=2.43 for females; RR=2.30 for males). This result is supported by past studies. Furthermore, birth location also plays a role in success: individuals born in New York and California are ~2x more likely to become entertainers, and those born in the South are ~1.5x more likely to become athletes. These results validate the proposed methodology of using Wikipedia to study life outcomes.
What's in Your Tweets? I Know Who You Supported in the UK 2010 General Election
Authors: Boutet Antoine , Kim Hyoungshick , Yoneki Eiko
Published: November 2022

Nowadays, the use of social media such as Twitter is necessary to monitor trends of people on political issues. As a case study, we collected the main stream of Twitter related to the 2010 UK general election during the associated period. We analyse the characteristics of the three main parties in the election. Also, we propose a simple and practical algorithm to identify the political leaning of users using the amount of Twitter messages which seem related to political parties. The experimental results showed that the best-performing classification method -- which uses the number of Twitter messages referring to a particular political party -- achieved about 86% classification accuracy without any training phase.
Factors Affecting End-User Satisfaction on Facebook
Authors: Sibona Christopher , Choi Jae Hoon
Published: November 2022

This study predicts end-user satisfaction on Facebook through two constructs: perceived ease-of-use and perceived usefulness. Usefulness was operationalized to reflect the perception that the site is helpful for friendship maintenance and social surveillance. The results show that Facebook users perceive the site to be both easy-to-use and useful. Perceived ease-of-use and perceived usefulness were statistically significant  predictors for satisfaction; the perceived usefulness of the site has a greater impact on satisfaction than perceived ease-of-use. Several control variables were included to adjust the results. Satisfaction is statistically significantly higher for users who have more friends and interact with more people compared to those who had fewer friends and fewer interactions. Satisfaction is not statistically different for males and females when accounting for users' perception of usefulness and ease-of-use. This study applies the IS success model developed for more utilitarian  systems to a hedonic social network site.
What Catches Your Attention? An Empirical Study of Attention Patterns in Community Forums
Authors: Wagner Claudia , Rowe Matthew , Strohmaier Markus , Alani Harith
Published: November 2022

Online community managers work towards building and managing communities around a given brand or topic. A risk imposed on such managers is that their community may die out and its utility diminish to users. Understanding what drives attention to content and the dynamics of discussions in a given community informs the community manager and/or host with the factors that are associated with attention. In this paper we gain insights into the idiosyncrasies that individual community forums exhibit in their attention patterns and how the factors that impact activity differ. We glean such insights by using logistic regression models for identifying seed posts and explore the effectiveness of a range of features. Our findings show that the discussion behaviour of different communities is clearly impacted by different factors.
More of a Receiver Than a Giver: Why Do People Unfollow in Twitter?
Authors: Kwak Haewoon , Moon Sue , Lee Wonjae
Published: November 2022

We propose a logistic regression model taking into account two analytically different sets of factors–structure and action. The factors include individual, dyadic, and triadic properties between ego and alter whose tie breakup is under consideration. From the fitted model using a large-scale data, we discover 5 structural and 7 actional variables to have significant explanatory power for unfollow. One unique finding from our quantitative analysis is that people appreciate receiving acknowledgements from others even in virtually unilateral communication relationships and are less likely to unfollow them: people are more of a receiver than a giver.
Semantic Social Networks Constructed by Topical Aspects of Conversations: An Explorative Study
Authors: Jang Jiyeon , Choi Jinhyuk , Jang Gwan , Myaeng Sung-Hyon
Published: November 2022

As the number of social networking services (SNS) and their users grow, so does the complexity of individual networks as well as the amount of information to be consumed by the users. Users of SNS exchange short and instantaneous messages interactively, which can be seen as conversations. We explore this conversational aspect of SNS and show how refined topic-based semantic social networks can be formed in order to reduce the complexity and information overload. Among other possibilities, we use the notion of topic diversity and topic purity of SNS conversations between two users and show different types of social relationships can be identified in that they break down a huge “syntactic” social network into topic-based ones based on different interaction types. Resulting semantic social networks can be useful in designing various targeted services on online social networks.
Differences in Language and Style Between Two Social Media Communities
Authors: Paris Cecile , Thomas Paul , Wan Stephen
Published: November 2022

Microblogs are increasingly used as communication channels for organisations and their related communities. In this work, we are interested in the effect of community on the resulting microblog language use. We analyse content from Twitter, examining tweets relating to two government organisations—one conducting scientific research, the other providing social services. We find that the two different communities have significant differences in style and language use, observing marked differences in formality and tone as measured by properties such as pronominal usage, orthographic convention, and use of Twitter features. We posit that these differences arise due to underlying differences in the communication goals of the two user groups. Tools working with Twitter, to extract and represent information, may therefore need different approaches in different domains.
Conversation Practices and Network Structure in Twitter
Authors: Rossi Luca , Magnani Matteo
Published: November 2022

The public by default nature of Twitter messages, together with the adoption of the #hashtag convention led, in few years, to the creation of a digital space able to host world-wide conversation on almost every kind of topic. From major TV shows to Natural disasters there is no contemporary event that does not have its own #hashtag to gather together the ongoing Twitter conversation. These topical discussions take place outside of the Twitter network made of followers and friends. Nevertheless this topical network is where many of the most studied phenomena take place. Therefore Twitter based communication exists on two almost autonomous levels: the Twitter network made of followers and friends that shows a certain level of stability and the topical network, characterized by a high level of contingency, that appears and disappears following the rhythm of a worldwide conversation. Despite the fact that this double nature of Twitter is widely recognized among scholars there is still little literature facing the relationships between these two networks. This paper presents the results of an empirical research aimed at discovering how the Twitter network is affected by what happens on the topical network. Does the participation in the same hashtag based conversation change the follower list of the participants? Is it possible to point out specific social behaviors that would produce a major gain of followers? Our conclusions are based on real data concerning the popular TV show Xfactor, that largely used Twitter as the official backchannel platform for its audience.
Mixed Membership Models for Exploring User Roles in Online Fora
Authors: White Arthur , Chan Jeffrey , Hayes Conor , Murphy Brendan
Published: November 2022

Discussion boards are a form of social media which allow users to discuss topics and exchange information in a complex manner, in a number of different settings. As the popularity of such message boards has increased, communities of users have emerged, and several prominent types of social role have been identified, such as Question Answerer, Celebrity, Discussion Person and Topic Initiator. Recent studies have noted the structural similarity of the egocentric network of users assigned the same role by qualitative criteria. In this paper a methodology is developed with which to cluster together users with similar ego-centric network structures. This is achieved using a mixed membership formulation which allows for the fact that different groups of users may have characteristics in common. The method is then applied to data taken from boards.ie, a medium sized message boards website. Prominent clusters of users are identified and discussed, and illustrative examples of user behaviour provided. The type of interaction, both locally and globally, taking place within forums is examined.
Catching the Long-Tail: Extracting Local News Events from Twitter
Authors: Agarwal Puneet , Vaithiyanathan Rajgopal , Sharma Saurabh , Shroff Gautam
Published: November 2022

Twitter, used in 200 countries with over 250 milliontweets a day, is a rich source of local news from aroundthe world. Many events of local importance are first reportedon Twitter, including many that never reach newschannels. Further, there are often only a few tweetsreporting each such event, in contrast with the largervolumes that follow events of wider significance. Eventhough such events may be primarily of local importance,they can also be of critical interest to some specificbut possibly far flung entities: For example, a firein a supplier’s factory half-way around the world maybe of interest even from afar. In this paper we describehow this ‘long tail’ of events can be detected in spite oftheir sparsity.We then extract and correlate informationfrom multiple tweets describing the same event. Ourgeneric architecture for converting a tweet-stream intoevent-objects uses locality sensitive hashing, classification,boosting, information extraction and clustering.Our results, based on millions of tweets monitored overmany months, appear to validate our approach and architecture:We achieved success-rates in the 80% rangefor event detection and 76% on event-correlation; we also reduced tweet-comparisons by 80% using LSH.
Evaluating Real-Time Search over Tweets
Authors: McCullough Dean , Lin Jimmy , Macdonald Craig , Ounis Iadh , McCreadie Richard
Published: November 2022

Twitter offers a phenomenal platform for the social sharing of information. We describe new resources that have been created in the context of the Text Retrieval Conference (TREC) to support the academic study of Twitter as a real-time information source. We formalize an information seeking task — real-time search — and offer a methodology for measuring system effectiveness. At the TREC 2011 Microblog Track, 58 research groups participated in the first ever evaluation of this task. We present data from the effort to illustrate and support our methodology.
Towards Social Semantic Journalism
Authors: Rahmanzadeh Heravi Bahareh , Boran Marie , Breslin John
Published: November 2022

User-generated content has become a valuable journalistic tool for news coverage and production. This convergence of new and old media, however, poses several challenges to established news organisations. Social media sites produce a wealth of data in the form of text, images and video that must be processed, compiled and verified within a very short timespan before being incorporated into a news story. This unstructured data that lies scattered across the web can be formalised and organised into a ‘web of data’ by Semantic Web technologies. Specifically, Semantic Web technologies have the potential to formalise and integrate artifacts produced and shared across the Social Web. Social Semantic Journalism proposes the utilisation of Semantic Web technologies, and specifically Social Semantic Web ontologies such as FOAF and SIOC, in the process of news production. This potentially provides a journalistic tool to assist in finding, aggregating and verifying user-generated content for news production.
SMILE: An Informality Classification Tool for Helping to Assess Quality and Credibility in Web 2.0 Texts
Authors: Mosquera Alejandro , Moreda Paloma
Published: November 2022

The data made available by Web 2.0 applications such as social networks, on-line chats or blogs have give access to multiples sources of information. Due to this dramatic increase in available information, the perception of quality and credibility plays an important role in social media, thus making necessary to discard low quality and uninteresting content. Moreover, the informal features of Web 2.0 texts such as emoticons, typos, slang or loss of formatting impact negatively on user perception regarding content quality and credibility. For this reason, this paper proposes the SMILE system, a novel unsupervised real-time tool for assessing user-generated content quality and credibility using informality levels. As a test case, we focus on Yahoo! Answers, a relevant Web 2.0 application by its amount of users, content and textual diversity. The results of our study show that informality analysis can be used as criteria to help assess the credibility and quality of Web 2.0 information sources.
Using Complex Event Processing for Modeling Semantic Requests in Real-Time Social Media Monitoring
Authors: Riemer Dominik , Stojanovic Ljiljana , Stojanovic Nenad
Published: November 2022

Social media analytics has been attracting considerable attention in both research and industry due to the increasing popularity of social media usage. As a subset, social media monitoring describes the process of continuous monitoring of a subject matter in social media. From our point of view, the key requirements for such systems are i) high throughput and real-time processing of incoming data, ii) a user-friendly way to define complex situations of interests that make use of formalized background knowledge and iii) capabilities to perform actions based on gained insights instead of a pure monitoring system. In this paper, we propose a system for (pro) active, real-time social media monitoring. Firstly, we describe the conceptual architecture of our system and necessary pre-processing steps. Secondly, we introduce our concept of semantic requests that is capable to extend event pattern definitions with background knowledge. Finally, we show the usefulness of this system in two different domains: Real-time political opinion tracking and proactive establishment of relationships with consumers in order to perform a new form of real-time marketing. The main advantage of our approach is a simplified, expressive way to formulate event patterns in social media applications.
Distilling Massive Amounts of Data into Simple Visualizations: Twitter Case Studies
Authors: Rios Miguel , Lin Jimmy
Published: November 2022

We present three Twitter case studies of distilling massive amounts of data into simple visualizations.
Empowering Civic Participation in the Policy Making Process through Social Media
Authors: Kleinfeld Robert , Bassbouss Louay , Alvertis Iosif , Gionis George
Published: November 2022

The Web as medium has a high significance in everyday life of the digital society. The circumstance of growing visibility of social media covers a high potential in the range of a more citizen-centric and socially-rooted policy making. These potentials call for novel tools with the capability to analyze society’s input and predict the possible impact of policies. The paper describes a prototype tool set for policy makers that utilizes social media technologies and methods to empower public engagement, enable cross-media platform publishing, feedback tracking / analysis and provide decision support.
FoodMood: Measuring Global Food Sentiment One Tweet at a Time
Authors: Dixon Natalie , Jakic Bruno , Lagerweij Roderick , Mooij Mark , Yudin Ekaterina
Published: November 2022

Do Happy Meals really make us happy? Do salads make us blue? Is cake our comfort? FoodMood is an interactive data visualisation project that gives citizens a rare opportunity to engage and reflect, acknowledge, and understand the connection between emotion, obesity and food. The project explores the opportunities presented by the data-sharing world of today’s cities using global English-language tweets about food coupled with sentiment analysis. It aims to gain a better understanding of global food consumption patterns and its impact on the daily emotional well-being of people against the backdrop of country data such as Gross Domestic Product (GDP) and obesity levels. A key finding is that tweets can be used to find a relationship between certain foods, food sentiment and obesity levels in countries. Overall FoodMood shows a majority positive sentiment towards food. Other findings, although constantly evolving, indicate trends such as: globally meat enjoys a high sentiment rating and is often tweeted about; fast-food companies dominate the food consumption landscapes of most countries’ tweets although not all of them enjoy equal sentiment ratings across countries. Ultimately, FoodMood reveals a hidden layer of meaningful digital, social, and cultural data that provide a basis for further analysis.
Content Analytics System for Social Customer Relationship Management
Authors: Nagarajan Meena , Contractor Danish , Dill Stephen , Ajmera Jitendra , Ahn Hyung-il , Verma Ashish , Denesuk Matthew
Published: November 2022

We propose to demonstrate a system that helps enterprises manage their social Customer Relationship Management (CRM) presence. Our system performs deep analytics over consumer posts and authors to help companies monitor their CRM presence, and to identify and respond to actionable posts. In this paper we share challenges and experiences in building this system. Findings yield a step change in the ability to implement social systems for customer care and leverage communities as effective proxies for CRM. This work also identifies novel knowledge and data mining challenges, such as identifying content that requires enterprise action, and prioritizing actionable posts based on various types of intent.
Preface
Authors: Kiciman Emre , Ellison Nicole , Hogan Bernie , Resnick Paul , Soboroff Ian
Published: November 2022

The seventh meeting of the International AAAI Conference on Weblogs and Social Media (ICWSM-13) was held in Cambridge, Massachusetts. The year 2013 promises to be a benchmark year for ICWSM. Thanks to the enthusiastic participation of our community, we received a record number of submissions, with a growth of 50 percent over the previous year. More than the quantity, however, the high quality of the submitted papers is the truest evidence that ICWSM is maturing in its role as a premier venue for social media research.
Keynote Addresses
Authors: Lazer David , Viegas Fernanda , Wattenberg Martin
Published: November 2022

The keynote addresses at the 2013 ICWSM conference are "The Challenges and Opportunities in Bridging Computer Science and Social Science in Studying Social Media" and "Visualizing Social Weather and Climate." Abstracts of the two talks have been published in the proceedings.
CrowdE: Filtering Tweets for Direct Customer Engagements
Authors: Chen Jilin , Cypher Allen , Drews Clemens , Nichols Jeffrey
Published: November 2022

Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools.
Families on Facebook
Authors: Burke Moira , Adamic Lada , Marciniak Karyn
Published: November 2022

This descriptive study of millions of US Facebook users documents "friending" and communication patterns, exploring parent-child relationships across a variety of life stages and gender combinations. Using statistical techniques on 400,000 posts and comments, we identify differences in how parents talk to their children (giving advice, affection, and reminders to call) compared to their other friends, and how they address their adult sons and daughters (talking about grandchildren and health concerns, and linguistically treating them like peers) compared to their teenage children. Parents and children have 20-30 mutual friends on the site, 19% of whom are relatives. Unlike previous findings on family communication, interaction frequency on Facebook does not decrease with geographic distance.
Automatic Summarization of Events from Social Media
Authors: Chua Freddy , Asur Sitaram
Published: November 2022

Social media services such as Twitter generate phenomenal volume of content for most real-world events on a daily basis. Digging through the noise and redundancy to understand the important aspects of the content is a very challenging task. We propose a search and summarization framework to extract relevant representative tweets from a time-ordered sample of tweets to generate a coherent and concise summary of an event. We introduce two topic models that take advantage of temporal correlation in the data to extract relevant tweets for summarization. The summarization framework has been evaluated using Twitter data on four real-world events. Evaluations are performed using Wikipedia articles on the events as well as using Amazon Mechanical Turk (MTurk) with human readers (MTurkers). Both experiments show that the proposed models outperform traditional LDA and lead to informative summaries.
Enhancing Technical Q&A Forums with CiteHistory
Authors: Fourney Adam , Ringel Morris Meredith
Published: November 2022

Software engineers often use Q&A forums like Stack Overflow and MSDN to ask and answer technical questions. Through a survey study and web browser log analysis, we find that both askers and answerers of technical forum questions typically conduct extensive online research before composing their posts. The inclusion of links to these research materials is beneficial to the forum participants, though post authors do not always include such citations. Based on these findings, we developed CiteHistory, a browser plugin that simplifies the process of including relevant search queries and URLs as bibliographic supplements to forum posts, and supports information re-finding for post authors. We discuss the results of a two-week deployment of CiteHistory with professional software engineers, which demonstrated that CiteHistory increased reference inclusion in posts, and offered auxiliary benefits as a personal research tracker.
The Where and When of Finding New Friends: Analysis of a Location-based Social Discovery Network
Authors: Chen Terence , Kaafar Mohamed , Boreli Roksana
Published: November 2022

With more people accessing Online Social Networks (OSN) using their mobile devices, location-based features have become an important part of the social networking. In this paper, we present the first measurement study of a new category of location-based online social networking services, a location-based social discovery (LBSD) network, that enables users to discover and communicate with nearby people. Unlike popular check-in-based social networks, LBSD allows users to publicly reveal their locations without being associ- ated to a specific “venue” and their usage is not influenced by the incentive mechanisms of the underlying virtual community. By analyzing over 8 million user profiles and around 150 million location updates collected from a popular new LBSD network, we first present the characteristics of spatial- temporal usage patterns of the observed users, showing that 40% of updates are from the user’s primary location and 80% are from their top 10 locations. We identify events that trigger bursts of growth in subscriber numbers, showing the importance of social media marketing. Finally, we investigate how usage patterns may be utilized to re-identify individuals with e.g. different identifiers or from datasets belonging to different online services. We evaluate re-identification by usage, spatial and spatial-temporal patterns and using a number of metrics and show that the best results can be achieved using location data, with a high accuracy: our experiments demonstrate that we can re-identify up-to 85% of users with a precision of 77% using monitored spatial data. Overall, we find that although users exhibit strong periodic behavior in their usage pattern and movements, the success rate of re-identification is highly dependent on the level of activeness and the lifetime of the users in the network.
Blind Men and the Elephant: Detecting Evolving Groups in Social News
Authors: Bandari Roja , Rahmandad Hazhir , Roychowdhury Vwani
Published: November 2022

We propose an automated and unsupervised methodology for a novel summarization of group behavior based on content preference. We show that graph theoretical community evolution (based on similarity of user preference for content) is effective in indexing these dynamics. Combined with text analysis that targets automatically-identified representative content for each community, our method produces a novel multi-layered representation of evolving group behavior. We demonstrate this methodology in the context of political discourse on a social news site with data that spans more than four years and find coexisting political leanings over extended periods and a disruptive external event that lead to a significant reorganization of existing patterns. Finally, where there exists no ground truth, we propose a new evaluation approach by using entropy measures as evidence of coherence along the evolution path of these groups. This methodology is valuable to designers and managers of online forums in need of granular analytics of user activity, as well as to researchers in social and political sciences who wish to extend their inquiries to large-scale data available on the web.
Sharing the Loves: Understanding the How and Why of Online Content Curation
Authors: Zhong Changtao , Shah Sunil , Sundaravadivelan Karthik , Sastry Nishanth
Published: November 2022

This paper looks at how and why users categorise and curate content into collections online, using datasets containing nearly all the relevant activities from Pinterest.com during January 2013, and Last.fm in December 2012. In addition, a user survey of over 25 Pinterest and 250 Last.fm users is used to obtain insights into the motivations for content curation and corroborate results. The data reveal that curation tends to focus on items that may not rank highly in popularity and search rankings. Yet, curated items exhibit their own skewed popularity, with the top few items receiving most of the attention; indicative of a synchronised community. We distinguish structured curation by active categorisation from a more passive bookmarking by `liking' an item, and find the former more prevalent for popularly curated items. Likes, however, are initially accumulated at a faster pace. Finally, we study the social value of content curation and show that curators attract more followers with consistent activity, and diversity of interests. Interestingly, our user study indicates a divided opinion on the relevance of the social network.
Characterizing Geographic Variation in Well-Being Using Tweets
Authors: Schwartz Hansen , Eichstaedt Johannes , Kern Margaret , Dziurzynski Lukasz , Lucas Richard , Agrawal Megha , Park Gregory , Lakshmikanth Shrinidhi , Jha Sneha , Seligman Martin , Ungar Lyle
Published: November 2022

The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association.
Artist Popularity: Do Web and Social Music Services Agree?
Authors: Bellogin Alejandro , De Vries Arjen , He Jiyin
Published: November 2022

Recommendations based on the most popular products in a catalogue is a common technique when information about users is scarce or absent. In this paper we explore different ways to measure popularity in the music domain; more specifically, we define four indices based on three social music services and on web clicks. Our study shows, first, that for most of the indices the popularity is a rather stable signal, since it barely changes over time; and second, that the ranking of popular artists is heavily dependent on the actual index used to measure the artist's popularity.
De-Anonymizing Users Across Heterogeneous Social Computing Platforms
Authors: Korayem Mohammed , Crandall David
Published: November 2022

Many people use multiple online and social computing platforms, and choose to share varying amounts of personal information about themselves depending on the context and type of site. For example, people may be willing to share personally-identifiable details (including their real name and date of birth) on a site like Facebook, but may withhold their identity on a dating site that may be widely viewed by strangers. We study the extent to which subtle correlations in a user's activity patterns across different sites may be used to infer that two accounts correspond to the same person. We study a variety of features, including similarity of temporal access patterns, textual content, geo-tags, and social connections, finding that even very weak signals yield surprisingly accurate de-anonymization results.
Organizers
Authors: Celli Fabio
Published: November 2022

List of organizers of the AAAI ICWSM Workshop on Computational Personality Recognition (Shared Task).