bluuebunny/crossref_metadata_2025 huggingface dataset:
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Authors: Alam Firoj , Qazi Umair , Imran Muhammad , Ofli Ferda
Published: November 2022

Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large volume, social media content is often too noisy for direct use in any application. Therefore, it is important to filter, categorize, and concisely summarize the available content to facilitate effective consumption and decision-making. To address such issues automatic classification systems have been developed using supervised modeling approaches, thanks to the earlier efforts on creating labeled datasets. However, existing datasets are limited in different aspects (e.g., size, contains duplicates) and less suitable to support more advanced and data-hungry deep learning models. In this paper, we present a new large-scale dataset with ~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19 disaster events that happened between 2016 and 2019. Moreover, we propose a data collection and sampling pipeline, which is important for social media data sampling for human annotation. We report multiclass classification results using classic and deep learning (fastText and transformer) based models to set the ground for future studies. The dataset and associated resources are publicly available at https://crisisnlp.qcri.org/humaid_dataset.html.
To Recommend or Not? A Model-Based Comparison of Item-Matching Processes
Authors: Chang Serina , Ugander Johan
Published: November 2022

Recommender systems are central to modern online platforms, but a popular concern is that they may be pulling society in dangerous directions (e.g., towards filter bubbles). However, a challenge with measuring the effects of recommender systems is how to compare user outcomes under these systems to outcomes under a credible counterfactual world without such systems. We take a model-based approach to this challenge, introducing a dichotomy of process models that we can compare: (1) a “recommender” model describing a generic item-matching process under a personalized recommender system and (2) an “organic” model describing a baseline counterfactual where users search for items without the mediation of any system. Our key finding is that the recommender and organic models result in dramatically different outcomes at both the individual and societal level, as supported by theorems and simulation experiments with real data. The two process models also induce different trade-offs: regularization improves the mean squared error of matches in both settings, but at the cost of less diverse (less radical) items chosen in the recommender model but more diverse (more radical) items chosen in the organic model. These findings provide a formal framework for how recommender systems may be fundamentally altering how we interact with content, in a world increasingly mediated by such systems.
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Authors: Cheng Lu , Guo Ruocheng , Candan Kasim , Liu Huan
Published: November 2022

Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews has been mainly focused on a single numerical cause, e.g., ratings or sentiment scores. We argue that such notions of causes entail three key limitations: they solely consider the effects of single numerical causes and ignore different effects of multiple aspects -- e.g., Food, Service -- embedded in the textual reviews; they assume the absence of hidden confounders in observational studies, e.g., consumers' personal preferences; and they overlook the indirect effects of numerical causes that can potentially cancel out the effect of textual reviews on business revenue. We thereby propose an alternative perspective to this single-cause-based effect estimation of online reviews: in the presence of hidden confounders, we consider multi-aspect textual reviews, particularly, their total effects on business revenue and direct effects with the numerical cause -- ratings -- being the mediator. We draw on recent advances in machine learning and causal inference to together estimate the hidden confounders and causal effects. We present empirical evaluations using real-world examples to discuss the importance and implications of differentiating the multi-aspect effects in strategizing business operations.
Post Approvals in Online Communities
Authors: Horta Ribeiro Manoel , Cheng Justin , West Robert
Published: November 2022

In many online communities, community leaders (i.e., moderators and administrators) can proactively filter undesired content by requiring posts to be approved before publication.
But although many communities adopt post approvals, there has been little research on its impact on community behavior.
Through a longitudinal analysis of 233,402 Facebook Groups, we examined
1) the factors that led to a community adopting post approvals and
2) how the setting shaped subsequent user activity and moderation in the group.
We find that communities that adopted post approvals tended to do so following sudden increases in user activity (e.g., comments) and moderation (e.g., reported posts).
This adoption of post approvals led to fewer but higher-quality posts.
Though fewer posts were shared after adoption, not only did community members write more comments, use more reactions, and spend more time on the posts that were shared, they also reported these posts less.
Further, post approvals did not significantly increase the average time leaders spent in the group, though groups that enabled the setting tended to appoint more leaders.
Last, the impact of post approvals varied with both group size and how the setting was used, e.g., group size mediates whether leaders spent more or less time in the group following the adoption of the setting.
Our findings suggest ways that proactive content moderation may be improved to better support online communities.
The Hipster Paradox in Electronic Dance Music: How Musicians Trade Mainstream Success off against Alternative Status
Authors: Jadidi Mohsen , Lietz Haiko , Samory Mattia , Wagner Claudia
Published: November 2022

The hipster paradox in Electronic Dance Music is the phenomenon that commercial success is collectively considered illegitimate while serious and aspiring professional musicians strive for it. We study this behavioral dilemma using digital traces of performing live and releasing music as they are stored in the Resident Advisor, Juno Download, and Discogs databases from 2001-2018. We construct network snapshots following a formal sociological approach based on bipartite networks, and we use network positions to explain success in regression models of artistic careers. We find evidence for a structural trade-off among autonomy and success. Musicians in EDM embed into exclusive performance-based communities for autonomy but, in earlier career stages, seek the mainstream for commercial success. Our approach highlights how Computational Social Science can benefit from a close connection of data analysis and theory.
Quantifying the Creator Economy: A Large-Scale Analysis of Patreon
Authors: Sanyoura Lana El , Anderson Ashton
Published: November 2022

In recent years, the “creator economy” has emerged as a disruptive force in creative industries. Independent creators can now reach large and diverse audiences through online platforms, and membership platforms have emerged to connect these creators with fans who are willing to financially support them. However, the structure and dynamics of how membership platforms function on a large scale remain poorly understood. In this work, we develop an analysis framework for the study of membership platforms and apply it to the complete set of Patreon pledges exceeding $2 billion since its inception in 2013 until the end of 2020. We analyze Patreon activity through three perspectives: patrons (demand), creators (supply), and the platform as a whole. We find several important phenomena that help explain how membership platforms operate. Patrons who pledge to a narrow set of creators are more loyal, but churn off the platform more often. High-earning creators attract large audiences, but these audiences are less likely to pledge to other creators. Over its history, Patreon diversified into many topics and launched higher-earning creators over time. Our analysis framework and results shed light on the functioning of membership platforms and have implications for the creator economy.
Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach
Authors: Sarwar Sheikh Muhammad , Murdock Vanessa
Published: November 2022

Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech. Hate speech presents a challenge as it is directed at different target groups using a completely different vocabulary. Further the authors of the hate speech are incentivized to disguise their behavior to avoid being removed from a platform. This makes it difficult to develop a comprehensive data set for training and evaluating hate speech detection models because the examples that represent one hate speech domain do not typically represent others, even within the same language or culture. We propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision.
What Elements of an Online Social Networking Profile Predict Target- Rater Agreement in Personality Impressions?
Authors: Evans David , Gosling Samuel , Carroll Anthony
Published: November 2022

Social-Networking Websites (SNWs) like Facebook and MySpace are playing an increasingly prevalent role in everyday social interactions. But very little is known about the effectiveness of the various profile elements in conveying information about the personality of the profile owner. Here we examine 5,303 impressions made on the basis of a specially designed social networking website (http://www.YouJustGetMe.com) and on the basis of Facebook profiles. Our findings suggest that profile owners are generally seen by others as they see themselves; that when raters are judging unknown targets, rater-target agreement is stronger for female (vs. male) targets and for female (vs. male) raters; and several specific elements of profiles are associated with increased or diminished levels of rater-target impression agreement. The findings are important because they are the first to show how impression agreement may be affected by specific elements in SNW profiles.
Polling the Blogosphere: A Rule-Based Approach to Belief Classification
Authors: Kessler Jason
Published: November 2022

The research described here is part of a larger project with the objective of determining if a writer believes a proposition to be true or false. This task requires a deep understanding of a proposition's semantic context, which is far beyond NLP's state of the art. In light of this difficulty, this paper presents a shallow semantic framework that addresses the sub-problem of finding a proposition's truth-value at the sentence level. The framework consists of several classes of linguistic elements that, when linked to a proposition through specific lexico-syntactic connectors, change its truth-value. A pilot evaluation of a system implementing this framework yields promising results.
Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs
Authors: Nallapati Ramesh , Cohen William
Published: November 2022

In this work, we address the twin problems of unsupervised topic discovery and estimation of topic specific influence of blogs. We propose a new model that can be used to provide a user with highly influential blog postings on the topic of the user's interest. We adopt the framework of an unsupervised model called Latent Dirichlet Allocation, known for its effectiveness in topic discovery. An extension of this model, which we call Link-LDA, defines a generative model for hyperlinks and thereby models topic specific influence of documents, the problem of our interest. However, this model does not exploit the topical relationship between the documents on either side of a hyperlink, i.e., the notion that documents tend to link to other documents on the same topic. We propose a new model, called Link-PLSA-LDA, that combines PLSA and LDA into a single framework, and explicitly models the topical relationship between the linking and the linked document. The output of the new model on blog data reveals very interesting visualizations of topics and influential blogs on each topic. We also perform quantitative evaluation of the model using log-likelihood of unseen data and on the task of link prediction. Both experiments show that that the new model performs better, suggesting its superiority over Link-LDA in modeling topics and topic specific influence of blogs.
Predicting Success and Failure in Weight Loss Blogs through Natural Language Use
Authors: Chun Cindy , Jones Clinton , Liu Alexander , Pennebaker James
Published: November 2022

We explore the emerging phenomenon of blogging about personal goals, and demonstrate how natural language processing tools can be used to uncover psychologically meaningful constructs in blogs. We describe features of a blog community (2638 blogs) devoted to weight loss. We compare several approaches to text analysis in predicting weight loss from natural language use in a subset of the blogs (258 users; over 13,000 entries). First, we use a bag of words approach to distinguish the degree to which individual words can predict success and failure. Next, we compare the results to a deductive word count and categorization tool, Linguistic Inquiry and Word Count. We discuss the theoretical significance of the words and word categories that distinguish between bloggers who succeed and those who fail in their weight loss attempts, along with the implications of automated text analysis in summarizing psychological features of blogs
Extracting Topics and Innovators Using Topic Diffusion Process in Weblogs
Authors: Furukawa Tadanobu , Matsuo Yutaka , Ohmukai Ikki , Ishizuka Mitsuru
Published: November 2022

We propose a ranking technique which extracts topics and innovators by analyzing that process. Our method identifies URLs of topics and the bloggers who trigger topic diffusion. Our assumption is that the strength of propagation of a topic is determined by the influences of topics and bloggers. This decomposition is attained through singular value decomposition (SVD); We construct a diffusion matrix: the first left and right singular vectors are regarded respectively as the influences of the topics and the bloggers. We show that our method can extract propagative topics (which is not bursty because of the media effect) as well as the influential bloggers.
StoryUpgrade: Finding Stories in Internet Weblogs
Authors: Gordon Andrew , Swanson Reid
Published: November 2022

The phenomenal rise of Internet weblogging has created new opportunities for people to tell personal stories of their life experience, and the potential to share these stories with those who can most benefit from reading them. One barrier to this new mode of storytelling is the lack of accessibility; existing Internet search tools are not tailored to the unique characteristics of this textual genre. In this paper we describe our efforts to develop a search engine specifically for the stories that appear in Internet weblogs, called StoryUpgrade. This application utilizes statistical text classification technologies to separate story content from other text in weblog entries, and facilitates searches for stories that are related to particular activities of interest.
Approximating the Community Structure of the Long Tail
Authors: Java Akshay , Joshi Anupam , Finin Tim
Published: November 2022

In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the "long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general.
Second Space: A Generative Model for the Blogosphere
Authors: Karandikar Amit , Java, Akshay , Joshi Anupam , Finin Tim , Yesha Yaacov , Yesha Yelena
Published: November 2022

Analysing complex natural phenomena often requires synthesized data that matches observed characteristics. Graph models are widely used in analyzing the Web in general, but are less suitable for modeling the Blogosphere. While blog networks resemble many properties of Web graphs, the dynamic nature of the Blogosphere, its unique structure and the evolution of the link structure due to blog readership and social interactions are not captured by the existing models. We describe an agent-based simulation model to construct blog graphs that exhibit properties similar to the real world blog networks in their degree distributions, degree correlation, clustering coefficient and reciprocity. The model can help researchers analyze the Blogosphere and facilitates the development and testing of new algorithms.
Analysis of Online Question-Answering Forums as Heterogeneous Networks
Authors: Murata Tsuyoshi , Ikeya Tomoyuki
Published: November 2022

Analyzing social interactions of CGM (Consumer Generated Media) is important for encouraging commnication among users. In the case of question-answering forums, users and QA boards constitute a heterogeneous network whose nodes are users/boards and edges are authorship. Discovering both user communities and board communities and finding correlations between them will clarify their characteristics. This paper describes an attempt for analyzing heterogeneous social networks obtained from Yahoo! Chiebukuro (Japanese Yahoo! Answers). A new measurement for the correlation between user communities and board communities is defined, and characteristics of discovered communities are analyzed using it.
Open Domain Collaborative Storytelling with Say Anything
Authors: Swanson Reid , Gordon Andrew
Published: November 2022

In this demonstration we present Say Anything, an open domain interactive storytelling application where an author's original story sentences are used to select subsequent sentences from a corpus of millions of stories extracted from Internet weblogs.
Does Showing Off Help to Make Friends? Experimenting a Sociological Game on Self-Exhibition and Social Networks
Authors: Aguiton Christophe , Cardon Dominique , Castelain Aymeric , Fremaux Pierre , Girard Hélène , Granjon Fabien , Nepote Charles , Smoreda Zbigniew , Trupia Dilara , Ziemlicki Cezary
Published: November 2022

The purpose of this study is to give an empirical glance on different forms of self-exposure on Social Network Sites (SNS). Based on a sociological on-line game, it explores the different tendencies in the self-exhibition shift and tries to find out how it is linked to different strategies in order to choose friends. In the first game, people have to select, among four ranked pictures, the one they would accept to publish on their personal webpage. In the second game, they had to choose friends by discovering, step by step, five informational attributes to their internet profile. Analysis of a sample of 12,354 French respondents shows a middle-range level of exhibition and isolates five different ways to expose oneself: Modest, Traditional Exhibition, Bodily Immodesty, Show-off and Provocative. Those different self-exhibition patterns lead to specific relational behaviors on the Internet. The study underlines socio-demographic differences (age, gender, diploma, and occupation) in self-disclosure practices and shows that exhibition on the Internet must be considered as a strategic performance.
Content Based Recommendation and Summarization in the Blogosphere
Authors: Hassan Ahmed , Radev Dragomir , Cho Junghoo , Joshi Amruta
Published: November 2022

This paper presents a stochastic graph based method for recommending or selecting a small subset of blogs that best represents a much larger set. within a certain topic. Each blog is assigned a score that reflects how representative it is. Blog scores are calculated recursively in terms of the scores of their neighbors in a lexical similarity graph. A random walk is performed on a graph where nodes represent blogs and edges link lexically similar blogs. Lexical similarity is measured using either the cosine similarity measure, or the Kullback-Leibler (KL) divergence. In addition, the presented method combines lexical centrality with information novelty to reduce redundancy in ranked blogs. Blogs similar to highly ranked blogs are discounted to make sure that diversity is maintained in the final rank. The presented method also allows us to include additional initial quality priors to assess the quality of the blogs, such as frequency of new posts per day and the text fluency measured by n-gram model probabilities, etc. We evaluate our approach using data from two large blog datasets. We measure the selection quality by the number of blogs covered in the network as calculated by an information diffusion model. We compare our method to other heuristic and greedy selection methods and show that it significantly outperforms them.
CourseRank: A Closed-Community Social System through the Magnifying Glass
Authors: Koutrika Georgia , Bercovitz Benjamin , Kaliszan Filip , Liou Henry , Garcia-Molina Hector
Published: November 2022

Social sites are extremely popular among users but user interactions in most sites revolve around relatively simple tasks, such as uploading resources, tagging and poking friends. We believe that social sites can go beyond simple interactions among individuals and offer valuable services to well-defined, closed, communities (e.g., an academic, corporate or scientific community). In this paper, we present an example of a closed-community social system, CourseRank, an educational and social site where Stanford students can explore course offerings and plan their academic program. We perform an analysis of 12 months worth of CourseRank data including user contributed information, such as ratings and comments, as well as information extracted from the user logs, and we analyze several aspects of user interactions and user-contributed content in the site, such as activity levels, user behavior and user content quality. Our findings provide useful insights with respect to the potential of closed-community social sites.
Self-Presentation of Personality During Online Profile Creation
Authors: Counts Scott , Stecher Kristin
Published: November 2022

When users present themselves in a social networking community, they are called on to create profiles that are representative and portray a desired image. In this paper, we examine the profile creation process. We explore what self-presentations are desired and how these presentations are created by having people rate their profile along personality dimensions after completing each attribute field in a mock social network-like profile. Findings indicate that people hope to convey personality traits through their online profiles and that they are able to create profiles they feel match their desired self-presentation. Free-form profile attributes best enable desired self presentations of traits, and only a few of these attributes are needed for sufficient self presentation online.
Regression-Based Summarization of Email Conversations
Authors: Ulrich Jan , Carenini Giuseppe , Murray Gabriel , Ng Raymond
Published: November 2022

In this paper we present a regression-based machine learning approach to email thread summarization. The regression model is able to take advantage of multiple gold-standard annotations for training purposes, in contrast to most work with binary classifiers.  We also investigate the usefulness of novel features such as speech acts. This paper also introduces a newly created and publicly available email corpus for summarization research.  We show that regression-based classifiers perform better than binary classifiers because they preserve more information about annotator judgements. In our comparison between different regression-based classifiers, we found that Bagging and Gaussian Processes have the highest weighted recall.
Stylometric Analysis of Bloggers’ Age and Gender
Authors: Goswami Sumit , Sarkar Sudeshna , Rustagi Mayur
Published: November 2022

We report results of stylometric differences in blogging for gender and age group variation. The results are based on two mutually independent features. The first feature is the use of slang words which is a new concept proposed by us for Stylometric study of bloggers. Slang is a non-dictionary word that has evolved with time due to its frequent and popular usage. For the second feature, we have analysed the variation in average length of sentences across various age groups and gender. These two features are then augmented with previous study results reported in literature for stylometric analysis of age and gender. The combined feature list enhances the accuracy by a remarkable extent in predicting age and gender. These experiments were done on a 20,000 blog corpus. Experimental results show that these features work well in detection of bloggers’ demography. However, gender determination is more accurate than age group detection over a data spread across all ages but the accuracy of age prediction increases if we sample data with remarkable age difference.
Connecting Corresponding Identities across Communities
Authors: Zafarani Reza , Liu Huan
Published: November 2022

One of the most interesting challenges in the area of social computing and social media analysis is the so-called community analysis. A well known barrier in cross-community (multiple website) analysis is the disconnectedness of these websites. In this paper, our aim is to provide evidence on the existence of a mapping among identities across multiple communities, providing a method for connecting these websites. Our studies have shown that simple, yet effective approaches, which leverage social media's collective patterns can be utilized to find such a mapping. The employed methods successfully reveal this mapping with 66% accuracy.
Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context
Authors: Barash Vladimir , Smith Marc , Getoor Lise , Welser Howard
Published: November 2022

Social media communities (e.g. Wikipedia, Flickr, Live Q&A) give rise to distinct types of content, foremost among which are relational content (discussion, chat) and factual content (answering questions, problem-solving). Both users and researchers are increasingly interested in developing strategies that can rapidly distinguish these types of content. While many text-based and structural strategies are possible, we extend two bodies of research that show how social context, and the social roles of answerers can predict content type.  We test our framework on a dataset of manually labeled contributions to Microsoft's Live Q&A and find that it reliably extracts factual and relational messages from the data.
StepGreen.org: Increasing Energy Saving Behaviors via Social Networks
Authors: Mankoff Jennifer , Fussell Susan , Dillahunt Tawanna , Glaves Rachel , Grevet Catherine , Johnson Michael , Matthews Deanna , Matthews H. , McGuire Robert , Thompson Robert , Shick Aubrey , Setlock Leslie
Published: November 2022

Decades of research have explored factors that can influence green behavior. However, much less is known about how technology in general, and social technologies in particular can motivate people to participate in green activities. In this paper we describe the goals, design and evaluation of StepGreen.org, a site intended to promote energy-saving behaviors. We present the results of a field study, during which participants chose to engage in different actions and reported when they had completed them. Our results suggest that motivating factors like public commitment and competition are effective, and better leveraging these factors will likely lead to even greater appeal and effectiveness. Our contribution is to create an understanding of the impact of different decisions on the success of StepGreen.org that can benefit the designers of other multifaceted, online systems for behavior change.
To Be a Star Is Not Only Metaphoric: From Popularity to Social Linkage
Authors: Stoica Alina , Couronne Thomas , Beuscart Jean - Samuel
Published: November 2022

The emergence of online platforms allowing to mix self publishing activities and social networking offers new possibilities for building online reputation and visibility. In this paper we present a method to analyze the online popularity that takes into consideration both the success of the published content and the social network topology. First, we adapt the Kohonen self organizing maps in order to cluster the users of online platforms depending on their audience and authority characteristics. Then, we perform a detailed analysis of the manner nodes are organized in the social network. Finally, we study the relationship between the network local structure around each node and the corresponding user’s popularity. We apply this method to the MySpace music social network. We observe that the most popular artists are centers of star shaped social structures and that it exists a fraction of artists who are involved in community and social activity dynamics independently of their popularity. This method based on a learning algorithm and on network analysis appears to be a robust and intuitive technique for a rich description of the online behavior.
Modeling Group Dynamics in Virtual Worlds
Authors: Shah Fahad , Sukthankar Gita , Usher Chris
Published: November 2022

In this study, we examine human social interactions within virtual worlds and address the question of how group interactions are affected by the game environment. To investigate this problem, we introduced a set of conversational agents into the social environment of Second Life, a massively multi-player online environment that allows users to construct and inhabit their own 3D world. Our agents were created to be sufficiently lifelike to casual observers, so as not to perturb neighboring social interactions. Using our partitioning algorithm, we separated continuous public chat logs from each region into separate conversations which were used to construct a social network of the participants. Unlike many groups formed in communities and workplaces, groups in Second Life can be rapidly-forming (arising from few interactions), persistent (remaining stable over a long period), and are less affected by socio-cultural influences. In this paper, we analyze regional differences in Second Life by measuring characteristics of the network as a whole, determined from the statistics mined from public conversations in the virtual world, rather than focusing on egocentric actors and their attributes.
Comparing Information Diffusion Structure in Weblogs and Microblogs
Authors: Yang Jiang , Counts Scott
Published: November 2022

To better understand and characterize the emerging social medium of microblogging we conducted a comparison between Twitter and a weblog network for their respective information diffusion structures. We found systematic differences between the two social media in their contribution, navigation, and interactive structural patterns. Findings revealed the unique role and characteristics of microblogs in the social media design space. Implications are discussed.
A Comparison of Generated Wikipedia Profiles Using Social Labeling and Automatic Keyword Extraction
Authors: Russell Terrell , Suh Bongwon , Chi Ed
Published: November 2022

In many collaborative systems, researchers are interested in creating representative user profiles. In this paper, we are particularly interested in using social labeling and automatic keyword extraction techniques for generating user profiles. Social labeling is a process in which users manually tag other users with keywords. Automatic keyword extraction is a technique that selects the most salient words to represent a user’s contribution. We apply each of these two profile generation methods to highly active Wikipedia editors and their contributions, and compare the results. We found that profiles generated through social labeling matches the profiles generated via automatic keyword extraction, and vice versa. The results suggest that user profiles generated from one method can be used as a seed or bootstrapping proxy for the other method.
Areca: Online Comparison of Research Results
Authors: Urbansky David , Muthmann Klemens , Kreisz Lars , Schill Alexander
Published: November 2022

To experiment properly, scientists from many researchareas need large sets of real world data. Information re-trieval scientists for example often need to evaluate theiralgorithms on a dataset or a gold standard. The availabil-ity of these datasets often is insufﬁcient and authors withthe same goal do not evaluate their approaches on thesame data. To make research results more transparentand comparable, we introduce Areca, an online portalfor sharing datasets and/or the results that were reachedwith the author’s algorithms on these datasets. Havingsuch an online comparison makes it easier to grasp thestate-of-the-art on certain tasks and drive research toimprove the results.
Digital Diasporas Atlas Exploration and Cartography of Diasporas in Digital Networks
Authors: Diminescu Dana , Matthieu Renault , Mehdi Bourgeois , Jacomy Mathieu
Published: November 2022

We will present the digital methodological chain and the tools we developed for building the Digital Diasporas Atlas which aims at mapping and analyzing the occupation of digital diasporas by migrant communities. Such a chain is composed of four intertwined steps: 1) equipped web exploration and corpus building; 2) data enrichment (location, languages, text-mining); 3) network visualization- manipulation and graph interpretation; 4) collaborative sharing of (raw) data and findings. The Digital Diasporas Atlas takes part in the project of introducing digital methods in humanities: promoting digitally equipped social sciences and developing an engineering informed by humanities.
Socio-Spatial Properties of Online Location-Based Social Networks
Authors: Scellato Salvatore , Noulas Anastasios , Lambiotte Renaud , Mascolo Cecilia
Published: November 2022

The spatial structure of large-scale online social networks has been largely unaccessible due to the lack of available and accurate data about people’s location. However, with the recent surging popularity of location-based social services, data about the geographic position of users have been available for the first time, together with their online social connections. In this work we present a comprehensive study of the spatial properties of the social networks arising among users of three main popular online location-based services. We observe robust universal features across them: while all networks exhibit about 40% of links below 100 km, we further discover strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We provide evidence that mechanisms akin to gravity models may influence how these social connections are created over space. Our results constitute the first large-scale study to unravel the socio-spatial properties of online location-based social networks.
Participation Maximization Based on Social Influence in Online Discussion Forums
Authors: Sun Tao , Chen Wei , Liu Zhenming , Wang Yajun , Sun Xiaorui , Zhang Ming , Lin Chin-Yew
Published: November 2022

In online discussion forums, users are more motivated to take part in discussions when observing other users’ participation—the effect of social influence among forum users. In this paper, we study how to utilize social influence for increasing the overall forum participation. To this end, we propose a mechanism to maximize user influence and boost participation by displaying forum threads to users. We formally define the participation maximization problem, and show that it is a special instance of the social welfare maximization problem with submodular utility functions and it is NP-hard. However, generic approximation algorithms is impracticable for real-world forums due to time complexity. Thus we design a heuristic algorithm, named Thread Allocation Based on Influence (TABI), to tackle the problem. Through extensive experiments using a dataset from a real-world online forum, we demonstrate that TABI consistently outperforms all other algorithms in maximizing participation. The results of this work demonstrates that current recommender systems can be made more effective by considering future influence propagations. The problem of participation maximization based on influence also opens a new direction in the study of social influence.
Exploring Millions of Footprints in Location Sharing Services
Authors: Cheng Zhiyuan , Caverlee James , Lee Kyumin , Sui Daniel
Published: November 2022

Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.
Latent Set Models for Two-Mode Network Data
Authors: DuBois Christopher , Foulds James , Smyth Padhraic
Published: November 2022

Two-mode networks are a natural representation for many kinds of relational data. These networks are bipartite graphs consisting of two distinct sets ("modes") of entities. For example, one can model multiple recipient email data as a two-mode network of (a) individuals and (b) the emails that they send or receive. In this work we present a statistical model for two-mode network data which posits that individuals belong to latent sets and that the members of a particular set tend to co-appear. We show how to infer these latent sets from observed data using a Markov chain Monte Carlo inference algorithm. We apply the model to the Enron email corpus, using it to discover interpretable latent structure as well as evaluating its predictive accuracy on a missing data task. Extensions to the model are discussed that incorporate additional side information such as the email's sender or text content, further improving the accuracy of the model.
Escaping Information Poverty through Internet Newsgroups
Authors: Hasler Laura , Ruthven Ian
Published: November 2022

This paper presents an ongoing investigation into how people use the Internet in an attempt to escape situations of information poverty. We consider posts from a variety of newsgroups which indicate that individuals feel they have nowhere else to go to find information or support for concerns crucial to their everyday lives. A qualitative content analysis is performed to examine three main issues: what information needs people posting to these groups have, who they hide these needs from in the ‘real world’, and why they feel they can only express their needs online. Preliminary findings show that information on health and relationships is most commonly sought. Needs are most often hidden from parents and family, and the main reasons for seeking information online are a lack of understanding and the fact that people simply don’t know where else to go. Our work confirms that newsgroups provide a means of expression for those who feel they have no local support available to them.
A Machine Learning Approach to Twitter User Classification
Authors: Pennacchiotti Marco , Popescu Ana-Maria
Published: November 2022

This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.
Location3: How Users Share and Respond to Location-Based Data on Social
Authors: Chang Jonathan , Sun Eric
Published: November 2022

In August 2010 Facebook launched Places, a location-based service that allows users to check into points of interest and share their physical whereabouts with friends. The friends who see these events in their News Feed can then respond to these check-ins by liking or commenting on them. These data consisting of the places people go and how their friends react to them are a rich, novel dataset. In this paper we first analyze this dataset to understand the factors that influence where users check in, including previous check-ins, similarity to other places, where their friends check in, time of day, and demographics. We show how these factors can be used to build a predictive model of where users will check in next. Then we analyze how users respond to their friends’ check-ins and which factors contribute to users liking or commenting on them. We show how this can be used to improve the ranking of check-in stories, ensuring that users see only the most relevant updates from their friends and ensuring that businesses derive maximum value from check-ins at their establishments. Finally, we construct a model to predict friendship based on check-in count and show that cocheck-ins has a statistically significant effect on friendship.
Social Influence, Popularity and Interestingness of Online Contents
Authors: Ung Hang
Published: November 2022

From user-generated content platforms to shopping portals, an important feature of websites is to identify which contents or items are the most popular and list them on the front page — assuming these contents are also the most interesting. In this paper, we question this assumption by studying data from a major news aggregator website where stories are contributed by users, and by focusing on the consequences of social influence. We first observe that the popularity of contents is correlated with the visibility of their authors, and can thus be explained by social influence. We then provide evidence of a feedback loop between the visibility of contributors and the popularity of their contributions, which increases the importance of social influence over time. Finally we quantify the effect of social influence in making each story popular and show that this effect tends to favor less interesting stories.
Trends in Social Media: Persistence and Decay
Authors: Asur Sitaram , A. Huberman Bernardo , Szabo Gabor , Wang Chunyan
Published: November 2022

Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users.The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We find that the resonance of the content with the users of the social network plays a major role in causing trends. Also, we observe that a majority of the content propagated to cause trends arise from traditional media sources with social media acting as a selective amplifier for them.
Information Propagation on the Web: Data Extraction, Modeling and Simulation
Authors: Nel François , Lesot Marie-Jeanne , Delavallade Thomas , Capet Philippe
Published: November 2022

This paper proposes a model of information propagation mechanisms on the Web, describing all steps of its design and use in simulation. First the characteristics of a real network are studied, in particular in terms of citation policies: from a network extracted from the Web by a crawling tool, distinct publishing behaviours are identified and characterised. The Zero Crossing model for information diffusion is then extended to increase its expressive power and allow it to reproduce this variety of behaviours. Experimental results based on a simulation validate the proposed extension.
MODEC — Modeling and Detecting Evolutions of Communities
Authors: Takaffoli Mansoureh , Sangi Farzad , Fagnan Justin , Zaiane Osmar
Published: November 2022

Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities.
Twitter Sentiment Analysis: The Good the Bad and the OMG!
Authors: Kouloumpis Efthymios , Wilson Theresa , Moore Johanna
Published: November 2022

In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.