bluuebunny/crossref_metadata_2025 huggingface dataset:
Mobilizing Lurkers with a Targeted Task
Authors: Farzan Rosta , DiMicco Joan , Brownholtz Beth
Published: November 2022

Users of online communities are commonly classified into active members versus lurkers. In this work we experiment with a method designed to encourage lurkers to share their acquired social capital with a community through lightweight contribution. The findings from our experiment demonstrate the importance of exploring new ways lurker populations can offer additional benefits to online communities.
“How Incredibly Awesome!” — Click Here to Read More
Authors: Ahn Hyung-il , Geyer Werner , Dugan Casey , Millen David
Published: November 2022

We investigate the impact of a discussion snippet's overall sentiment on a user's willingness to read more of a discussion. Using sentiment analysis, we constructed positive, neutral, and negative discussion snippets using the discussion topic and a sample comment from discussions taking place around content on an enterprise social networking site. We computed personalized snippet recommendations for a subset of users and conducted a survey to test how these recommendations were perceived. Our experimental results show that snippets with high sentiments are better discussion "teasers."
How to Become Famous in the Microblog World
Authors: Sakaki Takeshi , Matsuo Yutaka
Published: November 2022

Microblogging services are a social media platform in which users post their current states as a short text, similar to instant messaging (IM). Recently, microblogging services, including Twitter, have become extremely popular. Concomitantly with the growth of Twitter, several companies have begun to use Twitter as business tools. Some companies have succeeded in increasing sales. However, most companies are troubled at how to use Twitter and how to win popularity in the Twitter milieu. As described herein, we analyze how the number of followers changes in a certain period and determine which parameters dominate popularity in the Twitter world. We also classify Twitter users into groups based on social tagging and propose methods to become popular when using microblogging services.
A Comparison of Information Seeking Using Search Engines and Social Networks
Authors: Morris Meredith , Teevan Jaime , Panovich Katrina
Published: November 2022

The Web has become an important information repository; often it is the first source a person turns to with an informa-tion need. One common way to search the Web is with a search engine. However, it is not always easy for people to find what they are looking for with keyword search, and at times the desired information may not be readily available online. An alternative, facilitated by the rise of social media, is to pose a question to one‟s online social network. In this paper, we explore the pros and cons of using a social net-working tool to fill an information need, as compared with a search engine. We describe a study in which 12 participants searched the Web while simultaneously posing a question on the same topic to their social network, and we compare the results they found by each method.
Using Twitter During an Academic Conference: The #iswc2009 Use-Case
Authors: Letierce Julie , Passant Alexandre , Breslin John , Decker Stefan
Published: November 2022

We recently addressed a survey to Semantic Web researchers, in which Twitter was ranked in the top three services used in order to spread information and reach peer researchers, students, as well as a broader audience. In order to figure out how Twitter is really used by this community, we conducted an analysis of the Twitter feed of the recent 8th International Semantic Web Conference (#iswc2009).
The Wisdom of Bookies? Sentiment Analysis Versus. the NFL Point Spread
Authors: Hong Yancheng , Skiena Steven
Published: November 2022

The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60% of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53%).
User Acceptance of Micro-Blogging in the Enterprise
Authors: Zhang Jun , Qu Yan , Hansen Derek
Published: November 2022

In this paper, following the IT acceptance theory framework, we analyze factors that affect users’ acceptance of micro-blogging in a large corporate environment. We categorized users into 4 groups based on their posting and reading behaviors: Actives, Dabblers, Lurkers and Skeptics. Groups show different perceived benefits, cost concerns, and social influence factors. The work can help both practitioners and scholars build an initial understanding of user acceptance of micro-blogging in the Enterprise.
Making Project Team Recommendations from Online Information Sources
Authors: Earl Charles , Johnson Amos , Yelpaala Kaakpema , Good Travis
Published: November 2022

We are developing an Internet platform called MediaTeam that provides a marketplace connecting media content consumers to communities of media content creators. The platform is enabled by our method for automated assembly of virtual project teams. Media creators use the automated team assembler to quickly identify and team with collaborators. The team assembly platform factors in how the skills, work, and communication styles of team members complement each other into its team recommendation process. We are now testing the teaming and collaboration platforms with video creators and seek to launch by the summer.
Two Paths of Glory — Structural Positions and Trajectories of Websites within Their Topical Territory
Authors: Cardon Dominique , Fouetillou Guilhem , Roth Camille
Published: November 2022

We analyze the structure and processes of reputation building in a sample of Internet communities. Our approach uses a corpus of around 9,000 'active' French websites and blogs which have been manually labeled as belonging to a specific topical territory, such as "cooking", "crafts", or "politics". We propose a typology and map of the various structural positions that these websites may hold within their territory. This typology is essentially based on their authority status in terms of in- and out-going links and depending on whether those links come from/to outside their origin territory or not. Behind the unifying notion of "territory", we may therefore distinguish several distinct topological structures for each topic. Building upon dynamic data spanning over a period of 10 months, we are then able to exhibit characteristic trajectories of engagement into each territory and, more broadly, reputation building on the web. In particular, we suggest that there are two opposing approaches in building up online authority: either by progressively developing reputation from within the territory - thereby moving 'up' through a typical online ladder - or by exploiting fame that had been aquired prior to one's blogging activity. This descriptive model makes it possible to carry a sociological analysis of the variety of resources mobilized by bloggers to build their online reputation (relational authority, prestige, institutional support, commercial support, ...). We eventually illustrate both this phenomenon and specific successful trajectories to fame through a comprehensive study of a selection of topical territories.
An Assessment of Intrinsic and Extrinsic Motivation on Task Performance in Crowdsourcing Markets
Authors: Rogstadius Jakob , Kostakos Vassilis , Kittur Aniket , Smus Boris , Laredo Jim , Vukovic Maja
Published: November 2022

Crowdsourced labor markets represent a powerful new paradigm for accomplishing work. Understanding the motivating factors that lead to high quality work could have significant benefits. However, researchers have so far found that motivating factors such as increased monetary reward generally increase workers’ willingness to accept a task or the speed at which a task is completed, but do not improve the quality of the work. We hypothesize that factors that increase the intrinsic motivation of a task – such as framing a task as helping others – may succeed in improving output quality where extrinsic motivators such as increased pay do not. In this paper we present an experiment testing this hypothesis along with a novel experimental design that enables controlled experimentation with intrinsic and extrinsic motivators in Amazon’s Mechanical Turk, a popular crowdsourcing task market. Results suggest that intrinsic motivation can indeed improve the quality of workers’ output, confirming our hypothesis. Furthermore, we find a synergistic interaction between intrinsic and extrinsic motivators that runs contrary to previous literature suggesting “crowding out” effects. Our results have significant practical and theoretical implications for crowd work.
The Effect of Mobile Platforms on Twitter Content Generation
Authors: Perreault Mathieu , Ruths Derek
Published: November 2022

The increased popularity of feature-rich mobile devices in recent years has enabled widespread consumption and production of social media content via mobile devices. Because mobile devices and mobile applications change context within which an individual generates and consumes microblog content, we might expect microblogging behavior to differ depending on whether the user is using a mobile device. To our knowledge, little has been established about what, if any, effects such mobile interfaces have on microblogging. In this paper, we investigate this question within the context of Twitter, among the most popular microblogging platforms. This work makes three specific contributions. First, we quantify the ways in which user profiles are effected by the mobile context: (1) the extent to which users tend to be either fully non-mobile or mobile and (2) the relative activity of the mobile Twitter community. Second, we assess the differences in content between mobile and non-mobile tweets (posts to the Twitter platform). Our results show that mobile platforms produce very different patterns of Twitter usage. As part of our analysis, we propose and apply a classification system for tweets. We consider this to be the third contribution of this work. While other classification systems have been proposed, ours is the first to permit the independent encoding of a tweet’s form, content, and intended audience. In this paper we apply this system to show how tweets differ between mobile and non-mobile contexts. However, because of its flexibility and breadth, the schema may be useful to researchers studying Twitter content in other contexts as well.
Social Lens: Personalization Around User Defined Collections for Filtering Enterprise Message Streams
Authors: Daly Elizabeth , Muller Michael , Gou Liang , Millen David
Published: November 2022

Social media has led to a data explosion and has begun to play an ever increasing role as a valuable source of information and a mechanism for information discovery. The wealth of data highlights the need for methods to filter and sort information in order to allow users to discover useful information. Most traditional solutions focus on the user, either the user's social network, or a form of personalization based on collaborative filtering or predictive user modeling. This paper presents a novel algorithm to view information through a lens based on a user defined collection while excluding the attributes of the user from the analysis. As a result, the lens is transparent, tunable and sharable amongst users and, additionally allows both a reduction in information overload while discovering new related content.
Can the Ambiance of a Place be Determined by the User Profiles of the People Who Visit It?
Authors: Graham Lindsay , Gosling Samuel
Published: November 2022

Recently, several online social networking sites (OSNs; e.g., FourSquare) have emerged that allow people to record and broadcast their presence at physical locations. We examined the viability of using images of frequent patrons listed on an establishment’s FourSquare page to infer the ambiance of the establishment. In Study 1, 10 judges independently rated 25 bars and 24 cafes in Austin, Texas. Ratings were based on the profile pictures of 25 randomly selected persons who frequented each establishment. Ratings tapped ambient physical and psychological qualities (e.g., loud, creepy), typical-patron personalities (e.g., extraverted), and likely patron activities (e.g., dancing). Results indicated considerable inter-judge consensus across the attributes rated (mean ICCs averaged .32, .69. and .33 for the ambiance, patron-personality, and patron-activity variables respectively). In Study 2, a second team of 10 observers visited each target establishment during business hours and rated each location on the same set of variables. These ratings also showed strong inter-judge consensus (mean ICCs averaged .69, .79. and .62 for the ambiance, patron-personality, and patron-activity variables respectively). Correlations between the ratings obtained in Studies 1 and 2 showed surprisingly strong convergence suggesting that the profile photos conveyed valid information about the target establishments.
Creating Conversations: An Automated Dialog System
Authors: Gandy Lisa , Hammond Kristian
Published: November 2022

Online news sites often include a comments section where readers are allowed to leave their thoughts. These comments often contain interesting and insightful conversations between readers about the news article. However the richness of these conversations is often lost among other meaningless comments, and moreover all comments are found at the bottom of the web page. In this article, we discuss how our system inserts reader conversations into the news article to create a multimedia presentation called Shout Out. Shout Out features two virtual news anchors: one anchor reads the news and when appropriate the anchor pauses to have a conversation about the news with another anchor. This current iteration of Shout Out combines natural language techniques and reader conversations to create an engaging system.
You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube
Authors: Biel Joan-Isaac , Aran Oya , Gatica-Perez Daniel
Published: November 2022

An increasing interest in understanding human perception in social media has led to the study of the processes of personality self-presentation and impression formation based on user profiles and text blogs. However, despite the popularity of online video, we do not know of any attempt to study personality impressions that go beyond the use of text and still photos. In this paper, we analyze one facet of YouTube as a repository of brief behavioral slices in the form of personal conversational vlogs, which are a unique medium for self-presentation and interpersonal perception. We investigate the use of nonverbal cues as descriptors of vloggers' behavior and find significant associations between automatically extracted nonverbal cues for several personality judgments. As one notable result, audio and visual cues together can be used to predict 34% of the variance of the Extraversion trait of the Big Five model. In addition, we explore the associations between vloggers' personality scores and the level of social attention that their videos received in YouTube. Our study is conducted on a dataset of 442 YouTube vlogs and 2,210 annotations collected using Amazon's Mechanical Turk.
Exploring Text Virality in Social Networks
Authors: Guerini Marco , Strapparava Carlo , Ozbal Gozde
Published: November 2022

This paper aims to shed some light on the concept of virality - especially in social networks - and to provide new insights on its structure. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread, rather than to the influencers who spread it (b) virality is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised and they only partially overlap. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be independently predicted according to content features.
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena
Authors: Bollen Johan , Mao Huina , Pepe Alberto
Published: November 2022

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.
Automatic Group-Interactive Radio Using Social-Networks of Musicians
Authors: Fields Ben , Rhodes Christophe , D'Inverno Mark
Published: November 2022

Using request radio shows as a base interactive model, we present the Steerable Optimizing Self-Organized Radio (SoSoRadio) system as a prototypical music rec- ommender system with robust automatic playlist gen- eration. This work describes a web-based radio system that interacts with current listeners through the selection of periodic request songs from a pool of nominees.
Large-Scale Community Detection on YouTube for Topic Discovery and Exploration
Authors: Gargi Ullas , Lu Wenjun , Mirrokni Vahab , Yoon Sangho
Published: November 2022

Detecting coherent, well-connected communities in large graphs provides insight into the graph structure and can serve as the basis for content discovery. Clustering is a popular technique for community detection but global algorithms that examine the entire graph do not scale. Local algorithms are highly parallelizable but perform sub-optimally, especially in applications where we need to optimize multiple metrics. We present a multi-stage algorithm based on local-clustering that is highly scalable, combining a pre-processing stage, a lo- cal clustering stage, and a post-processing stage. We apply it to the YouTube video graph to generate named clusters of videos with coherent content. We formalize coverage, co- herence, and connectivity metrics and evaluate the quality of the algorithm for large YouTube graphs. Our use of local algorithms for global clustering, and its implementation and practical evaluation on such a large scale is a ﬁrst of its kind.
“Dancing with the Stars,” NBA Games, Politics: An Exploration of Twitter Users’ Response to Events
Authors: Popescu Ana-Maria , Pennacchiotti Marco
Published: November 2022

Microblogging services such as Twitter offer great opportunities for analyzing the reactions of a wide audience with respect to current events. In this paper, we explore the correlation between types of user engagement and events centered around celebrities (e.g., personal or professional events involving Actors, Musicians, Politicians, Athletes).
Sentiment Flow Through Hyperlink Networks
Authors: Miller Mahalia , Sathi Conal , Wiesenthal Daniel , Leskovec Jure , Potts Christopher
Published: November 2022

How does sentiment flow through hyperlink networks? Earlier work on hyperlink networks has focused on the structure of the network, often modeling posts as nodes in a directed graph in which edges represent hyperlinks. At the same time, sentiment analysis has largely focused on classifying texts in isolation. Here we analyze a large hyperlinked network of mass media and weblog posts to determine how sentiment features of a post affect the sentiment of connected posts and the structure of the network itself. We explore the phenomena of sentiment flow through experiments on a graph containing nearly 8 million nodes and 15 million edges. Our analysis indicates that (1) nodes are strongly influenced by their immediate neighbors, (2) deep cascades lead complex but predictable lives, (3) shallow cascades tend to be objective, and (4) sentiment becomes more polarized as depth increases.
Beyond Financial Support: Helping Citizens in Welfare Programs
Authors: Colineau Nathalie , Paris Cecile
Published: November 2022

Governments are turning to Social Media as a way to engage citizens in public policies through online debates and discussion forums. Together with the communication team of a government department, we are exploring another facet of government-citizens communication, and another opportunity for governments to exploit Social Media. In particular, we are investigating whether online communities could become a new channel to support specific groups of citizens. In this model, a government would facilitate the creation of online communities for specific cohorts of people sharing goals and needs as well as act as mediator. This model would enable governments to capitalise on the power of crowd-sourcing and the social capital that gets created through such communities to provide social and emotional peer-support. These communities would also serve to provide direct feedback on social security policies. We propose to explore the issues that arise in this context.
GlobalIdentifier: Unexpected Personal Social Content with Data on the Web
Authors: Paradesi Sharon , Shih Fuming
Published: November 2022

The past year has seen a growing public awareness of the privacy risks of social networking through personal information that people voluntarily disclose. A spotlight has accordingly been turned on the disclosure policies of social networking sites and on mechanisms for restricting access to personal information on Facebook and other sites. But this is not sufficient to address privacy concerns in a world where Web-based data mining tools can let anyone infer information about others by combining data from multiple sources. To illustrate this, we are building a demonstration data miner, GlobalInferencer, that makes inferences about an individual?s lifestyle and other behavior. GlobalInferencer uses linked data technology to perform unified searches across Facebook, Flickr, and public data sites. It demonstrates that controlling access to personal information on individual social networking sites is not an adequate framework for protecting privacy, or even for supporting valid inferencing. In addition to access restrictions, there must be mechanisms for maintaining the provenance of information combined from multiple sources, for revealing the context within which information is presented, and for respecting the accountability that determines how information should be used.
Preface
Authors: Breslin John , Shanahan James , Ellison Nicole , Tufekci Zeynep
Published: November 2022

Research into social media sits at the inter- section of several fields in both computing and social science, so since its inception, ICWSM has always had an interdisciplinary flavor. As you read through the program, you will see that this is reflected not just in a balance in pa- pers from several fields, but also within the contributions themselves, since many of the papers and posters demonstrate excellence in multiple areas. We believe this increasing inte- gration reflects a new level of maturity in the field: investigators have moved beyond simply applying their trusted methods to social media topics, and have started to implement the ideas and approaches that are best suited to their re- search questions.
Cross-Community Influence in Discussion Fora
Authors: Belák Václav , Lam Samantha , Hayes Conor
Published: November 2022

Online discussion fora have become an important cultural and business asset in the context of many services provided by both non-profit organizations and enterprises. In order to keep and eventually increase the value these systems deliver to their users, it is often necessary to moderate or even manage their dynamics. One way to do this efficiently is to focus primarily on the most influential actors in the system. However, identifying such users becomes increasingly hard with systems where there is a continuously growing large user base. We show that analysis and explanation of influence on the cross-community level is a promising way to provide a coarse-grained picture of a potentially very large system and that it may enable its stakeholders to find groups through which the system can be efficiently influenced, or it can help them to identify and avoid activity considered as malicious. In order to achieve that, we present a novel framework for cross-community influence analysis, which is evaluated on 10 years of data from the largest Irish online discussion system Boards.ie.
War Versus Inspirational in Forrest Gump: Cultural Effects in Tagging Communities
Authors: Dong Zhenhua , Shi Chuan , Sen Shilad , Terveen Loren , Riedl John
Published: November 2022

People from different cultures vary in cognition, emotion, and behavior. We explore cultural differences in a tagging system. We developed a model of cultural differences and performed a controlled empirical study with American and Chinese subjects to investigate questions that arise from the model. American and Chinese subjects differed in many ways: the number and types of tags they applied; the extent to which they applied suggested tags or entered new tags of their own; and how often they applied tags that originated from a different culture. Our results are consistent with theories of cultural differences between Asian and Western cultures. Our findings suggest new opportunities and mechanisms for shaping user behavior to produce useful tag repositories.
Evolution of Experts in Question Answering Communities
Authors: Pal Aditya , Chang Shuo , Konstan Joseph
Published: November 2022

Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.
Towards Analyzing Micro-Blogs for Detection and Classification of Real-Time Intentions
Authors: Banerjee Nilanjan , Chakraborty Dipanjan , Joshi Anupam , Mittal Sumit , Rai Angshu , Ravindran Balaraman
Published: November 2022

Micro-blog forums, such as Twitter, constitute a powerful medium today that people use to express their thoughts and intentions on a daily, and in many cases, hourly, basis. Extracting ‘Real-Time Intention’ (RTI) of a user from such short text updates is a huge opportunity towards web personalization and social net- working around dynamic user context. In this paper, we explore the novel problem of detecting and classifying RTIs from micro-blogs. We find that employing a heuristic based ensemble approach on a reduced dimension of the feature space, based on a wide spectrum of linguistic and statistical features of RTI expressions, achieves significant improvement in detect- ing RTIs compared to word-level features used in many social media classification tasks today. Our solution approach takes into account various salient characteristics of micro-blogs towards such classification – high dimensionality, sparseness of data, limited context, grammatical in-correctness, etc.
Weblog Analysis for Predicting Correlations in Stock Price Evolutions
Authors: Kharratzadeh Milad , Coates Mark
Published: November 2022

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S\&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future.
Impact and Diffusion of Sentiment in Political Communication – An Empirical Analysis of Political Weblogs
Authors: Dang-Xuan Linh , Stieglitz Stefan
Published: November 2022

In this paper, we examine whether sentiment of political blog entries is associated with increased feedback in terms of the quantity of triggered comments and whether the diffusion of sentiment might take place in the political blogosphere. Based on a data set of approximately 17,000 blog entries from the 60 most important German political blogs, we find that blog entries with either more positive or more negative overall sentiment tend to receive significantly more comments compared to sentiment-neutral or mixed-sentiment entries. Furthermore, our results show that positive as well as negative emotions might diffuse in the subsequent comments to corresponding blog entries.
Finding Influential Authors in Brand-Page Communities
Authors: Purohit Hemant , Ajmera Jitendra , Joshi Sachindra , Verma Ashish , Sheth Amit
Published: November 2022

Enterprises are increasingly using social media forums to engage with their customer online- a phenomenon known as Social Customer Relation Management (Social CRM). In this context, it is important for an enterprise to identify “influential authors” and engage with them on a priority basis. We present a study towards finding influential authors on Twitter forums where an implicit network based on user interactions is created and analyzed. Furthermore, author profile features and user interaction features are combined in a decision tree classification model for finding influential authors. A novel objective evaluation criterion is used for evaluating various features and modeling techniques. We compare our methods with other approaches that use either only the formal connections or only the author profile features and show a significant improvement in the classification accuracy over these baselines as well as over using Klout score.
Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors
Authors: Al Zamal Faiyaz , Liu Wendy , Ruths Derek
Published: November 2022

In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user.
Organizers
Authors: Zubiaga Arkaitz
Published: November 2022

Organizers of the Real-Time Analysis and Mining of Social Streams workshop.
Web-Based Visual Analytics for Social Media
Authors: Best Daniel , Bruce Joseph , Dowson Scott , Love Oriana , McGrath Liam
Published: November 2022

Social media provides a rich source of data that reflects current trends on a multitude of topics. The data can be harvested from Twitter, Facebook, blogs, and other social applications. The high rate of adoption of social media has created a domain that is difficult to analyze, due to the ever-expanding volume of data. Information visualization is key in drawing out features of interest in social media. The Scalable Reasoning System is an application that couples a back-end server equipped with analysis algorithms and an intuitive visual in- terface to allow for investigation. We provide a componentized system that can be rapidly adapted to user needs. The in- formation in which they are most interested is featured prominently in the application. As an example, we have developed a weather and traffic monitoring application for use by emergency operators in the city of Seattle.
#Londonsburning: Integrating Geographic, Topical and Social Information during Crisis
Authors: Glasgow Kimberly , Ebaugh Alison , Fink Clayton
Published: November 2022

Social media, such as microblogging, is a potentially powerful medium through which the city and its citizens can connect, particularly during times of crisis or extreme events. Yet neither the city nor its citizens are monolithic entities. We propose methods to integrate geographic, topical, and social information and behavior to improve situational understanding throughout the city, and enable more effective public and official response. We demonstrate this approach using Twitter data from the 2011 London riots.
ICWSM 2013 Sponsors
Authors: Kiciman Emre
Published: November 2022

List of sponsors of the ICWSM 2013 conference.
Opinion Fraud Detection in Online Reviews by Network Effects
Authors: Akoglu Leman , Chandy Rishi , Faloutsos Christos
Published: November 2022

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.
The Value of Completing Crowdfunding Projects
Authors: Wash Rick
Published: November 2022

Crowdfunding systems are social media websites that allow people to donate small amounts of money that add up to fund valuable larger projects. These websites are structured around projects: finite campaigns with well-defined goals, end dates, and completion criteria.  We use a dataset from an existing crowdfunding website --- the school charity Donors Choose --- to understand the value of completing projects. We find that completing a project is an important act that leads to larger donations (over twice as large), greater likelihood of returning to donate again, and few projects that expire close but not complete. A conservative estimate suggests that this completion bias led to over $15 million in increased donations to Donors Choose, representing approximately 16\% of the total donations for the period under study. This bias suggests that structuring many types of collaborative work as a series of projects might increase contribution significantly.
Touch Screens for Touchy Issues: Analysis of Accessing Sensitive Information from Mobile Devices
Authors: Pelleg Dan , Savenkov Denis , Agichtein Eugene
Published: November 2022

Smartphones, and other similar devices, are ideal for private activities: They are carried on the body and can be physically secured from prying eyes. They work anywhere. And they are not typically shared among family members or classmates, making them perfect for searching and exploring sensitive information - both via web search or through social information seeking. This paper validates these intuitions empirically, and builds on these observations to investigate the expression of sensitive information needs, such as inquiring about unplanned pregnancies, among users of mobile devices. We examine a large set of queries from the United States (4.2 million), submitted to Yahoo! Answers, and compare these to the 6.7 million queries submitted to Yahoo! search. Focusing on community question answering allows much richer analysis of the way information needs are expressed, setting our work apart form previous studies. For the first time, we empirically show that people prefer to express sensitive needs on mobile devices, as this manifests at both the lexical level and the semantic level, using a pre-defined taxonomy of topics. Further, we find that preference for mobile devices for sensitive topics holds true even when controlling for age and gender, which is facilitated by the large sample of users in our study (1.5 million). In particular, we show that young users, especially females, are more likely to inquire on sensitive issues, and are more likely to do this from locations distant from their common place of web access. To our knowledge, this is the largest study to date of mobile social information seeking, and is unique in terms of demographic diversity, the granularity of information needs examined, and the analysis of the location-dependence of sensitive information needs.
Perception Differences between the Depressed and Non-Depressed Users in Twitter
Authors: Park Minsu , McDonald David , Cha Meeyoung
Published: November 2022

One's state of mind will influence her perception of the world and people within it. In this paper, we explore attitudes and behaviors toward online social media based on whether one is depressed or not. We conducted semi-structured face-to-face interviews with 14 active Twitter users, half of whom were depressed and the other half non-depressed. Our results highlight key differences between the two groups in terms of perception towards online social media and behaviors within such systems. Non-depressed individuals perceived Twitter as an information consuming and sharing tool, while depressed individuals perceived it as a tool for social awareness and emotional interaction. We discuss several design implications for future social networks that could better accommodate users with depression and provide insights towards helping depressed users meet their needs through online social media.
Para 'Normal' Activity: On the Distribution of Average Ratings
Authors: Dalvi Nilesh , Kumar Ravi , Pang Bo
Published: November 2022

In this paper we study the distribution of average user rating of entities in three different domains: restaurants, movies, and products. We find that the distribution is heavily skewed, closely resembling a log-normal in all the cases. In contrast, the distribution of average critic rating is much closer to a normal distribution. We propose user selection bias as the underlying behavioral phenomenon causing this disparity in the two distributions. We show that selection bias can indeed lead to a skew in the distribution of user ratings even when we assume the quality of entities are normally distributed. Finally, we apply these insights to the problem of predicting the overall rating of an entity given its few initial ratings, and obtain a simple method that outperforms strong baselines.
Crowdphysics: Planned and Opportunistic Crowdsourcing for Physical Tasks
Authors: Sadilek Adam , Krumm John , Horvitz Eric
Published: November 2022

Research on human computation and crowdsourcing has concentrated on tasks that can be accomplished remotely over the Internet. We introduce a general class of problems we call crowdphysics (CP)---crowdsourcing tasks that require people to collaborate and synchronize both in time and physical space. As an illustrative example, we focus on a crowd-powered delivery service---a specific CP instance where people go about their daily lives, but have the opportunity to carry packages to be delivered to specific locations or individuals. Each package is handed off from person to person based on overlaps in time and space until it is delivered. We formulate CP tasks by reduction to a graph-planning problem, and analyze the performance using a large sample of geotagged tweets as a proxy for people's location. We show that packages can be delivered with remarkable speed and coverage. These results hold for the case when we know people's future locations and also when routing without global knowledge, making only local greedy decisions. To our knowledge, this is the first empirical evidence that dynamic networks of mobile individuals are highly navigable.
Recommending Fresh URLs Using Twitter Lists
Authors: Yamaguchi Yuto , Amagasa Toshiyuki , Kitagawa Hiroyuki
Published: November 2022

Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly.
Rewarding Participation in Social Media Enabled Communities of Practice
Authors: Allen Warren
Published: November 2022

Tacit knowledge is difficult to objectify, whether the goal of doing so is to test, share, or technologically reproduce it. Therefore, extending the traditionally skills-based I.T. certification practice to the assessment of social learning that occurs in Communities of Practice will be a challenge. My research approaches this challenge by examining the role of social media and related technologies, information systems, and artifacts in the mediation of knowledge and expertise across the boundary between workplace-based Communities of Practice and external professional communities. Findings from this empirical case study of the Microsoft MVP Award implicate certain design considerations into the design of social media within the workplace.
Assemblage of Social Technologies and Informal Knowledge Sharing
Authors: Jarrahi Mohammad Hossein
Published: November 2022

This study focuses on the ways in which social technologies as a whole facilitate informal knowledge sharing in the workplace. Social technologies include both common technologies such as email, phone and instant messenger and emerging social networking technologies, often known as social media or Web 2.0, such as blogs, wikis, public social networking sites (i.e., Facebook, Twitter, and LinkedIn), enterprise social networking technologies, etc. To understand the role of social technologies in informal knowledge practices, we pursue a field study of knowledge workers in consulting firms to investigate the role of social technologies in their informal knowledge sharing practices. Findings highlight five knowledge practices motivated by different knowledge problems and supported by the use of multiple social technologies.
Discover Diamonds-in-the-Rough Using Interactive Visual Analytics System-Tweets as a Collective Diary of the Occupy Movement
Authors: Dou Wenwen , Wang Derek , Ma Zhiqiang , Ribarsky William
Published: November 2022

The phenomenally wide-adoption of social media has stimulated a new means in organizing and carrying-out modern social movements. Exemplified by the Occupy Movement (OM), rich information, including protest-related events and people's responses to those events, is posted and shared through social media sites such as Twitter. However, it is quite challenging to make sense of such valuable information in a collective manner, as it is often submerged by all the other content on Twitter. In this case study, we demonstrate the combination of computational methods (e.g., topic modeling and event detection) and interactive visual analytics in facilitating users to examine how relevant tweets can reflect a collective view of a social movement. In particular, we focus on discovering and associating key events throughout the OM. Based on the event frequencies, our system helps users to divide the movement into three distinct stages. Information regarding "what" the events were about, "when" and "where" the events occurred, and "who" were involved is extracted from the tweets to describe each stage of the movement. The resulting case studies show that we can indeed construct a collective diary of the social movement by analyzing events extracted from the content of the tweets.
Visualizing User-Defined, Discriminative Geo-Temporal Twitter Activity
Authors: Weber Ingmar , Garimella Venkata Rama Kiran
Published: November 2022

We present a system that visualizes geo-temporal Twitter activity. The distinguishing features our system offers include, (i) a large degree of user freedom in specifying the subset of data to visualize and (ii) a focus on *discriminative* patterns rather than high volume patterns. Tweets with precise GPS co-ordinates are assigned to geographical cells and grouped by (i) tweet language, (ii) tweet topic, (iii) day of week, and (iv) time of day. The spatial resolutions of the cells is determined in a data-driven manner using quad-trees and recursive splitting. The user can then choose to see data for, say, English tweets on weekend evenings for the topic "party". This system has been implemented for 1.8 million geo-tagged tweets from Qatar (http://qtr.qcri.org/) and for 4.8 million geo-tagged tweets from New York City (http://nyc.qcri.org/) and can be easily extended to other cities/countries.
Preface
Authors: Adar Eytan , Resnick Paul
Published: November 2022

Welcome to the eighth meeting of the International Conference on Weblogs and Social Media (ICWSM-14) held in Ann Arbor, Michigan. With the help of the whole community — authors, reviewers, sponsors, steering committee and conference organizers — we have seen ICWSM evolve into the premier venue for social media research.
Tutorial Program
Authors: Adar Eytan , Resnick Paul
Published: November 2022

Descriptions of the tutorials presented at the Eighth International AAAI Conference on Weblogs and Social Media