bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Outcome for Collaborative Featured Article Nomination in Wikipedia
Authors: Hu Meiqun , Lim Ee-Peng , Krishnan Ramayya
Published: November 2022

In Wikipedia, good articles are wanted. While Wikipedia relies on collaborative effort from online volunteers for quality checking, the process of selecting top quality articles is time consuming. At present, the duty of decision making is shouldered by only a couple of administrators. Aiming to assist in the quality checking cycles so as to cope with the exponential growth of online contributions to Wikipedia, this work studies the task of predicting the outcome of featured article (FA) nominations.  We analyze FA candidate (FAC) sessions collected over a period of 3.5 years, and examine the extent to which consensus has been practised in this process.  We explore the use of interaction features between FAC reviewers to learn SVM classifiers to predict the nomination outcome.  We find that, calibrating the individual user's polarity of opinions as features improves the prediction accuracy significantly.
High-level Features for Learning Subjective Language across Domains
Authors: Dias Gaël , Lambov Dinko , Noncheva Veska
Published: November 2022

In this paper, we propose to study the characteristics for analyzing subjective content in documents. For that purpose, we present and evaluate a novel method based on level of abstraction of nouns. By comparing state-of-the-art features and the level of abstraction of nouns between three annotated corpora and texts downloaded from Wikipedia and Web Blogs, we show that, building data sets for the classification of opinionated texts can be done automatically from the web, at the document level. Moreover, we present accuracy levels within domains of 96.5% and across domains of 74.5%.
The Use of Weblogs in Spanish Mainstream Media
Authors: Palomo Torres Maria , Meso Ayerdi Koldo
Published: November 2022

In this paper we aim to draw a picture of what is happening nowadays in Spanish mainstream cybermedia from the point of view of new ways of participation and, more concretely, we analyze remarkable weblogs signed by journalists and/or guests, in order to determine the author’s profile. A survey answered by 50 per cent of these bloggers let us know why the use of weblogs represents the key to media success in the online sphere.
Detecting Topic Drift with Compound Topic Models
Authors: Knights Dan , Mozer Michael , Nicolov Nicolas
Published: November 2022

The Latent Dirichlet Allocation topic model of Blei, Ng, and Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.
The Distribution of Online Reputation: Audience and Influence of Musicians on MySpace
Authors: Beuscart Jean-Samuel , Couronné Thomas
Published: November 2022

The user-generated content platforms like MySpace, YouTube or Flickr produce emergent sociability patterns and behaviors, which are well observed in various studies. Few of them deal with the nature of the reputation in these large social networks. In this work, we extract a sample of MySpace Music by parsing the artists' pages and following the "best friend" links. The study of the network shows that the online fame is composed by two independent features: the audience, dominated by the major companies' artists, and the influence, more associated with independent labels. We observe that most of the audience is focused on a few stars: the tail of MySpace Music is long, but flat. The network structure shows that artists tend to link to artists of similar reputation.
Considering the Sources: Comparing Linking Patterns in Usenet and Blogs
Authors: McGlohon Mary , Hurst Matthew
Published: November 2022

Usenet is a decentralized discussion community predating blogs by decades. Just as there are a wide range of political blogs, many Usenet sub-communities focus on politics. However, we find these two communities are very different in terms of source content. In this work we compare linking patterns of the political Usenet with a well-known political blog and news aggregator, Memeorandum, with respect to coverage of news and blog sources, attention of stories, and timeliness of links.
TweetMotif: Exploratory Search and Topic Summarization for Twitter
Authors: O'Connor Brendan , Krieger Michel , Ahn David
Published: November 2022

We present TweetMotif, an exploratory search applica- tion for Twitter. Unlike traditional approaches to in- formation retrieval, which present a simple list of mes- sages, TweetMotif groups messages by frequent signif- icant terms — a result set’s subtopics — which facili- tate navigation and drilldown through a faceted search interface. The topic extraction system is based on syn- tactic ﬁltering, language modeling, near-duplicate de- tection, and set cover heuristics. We have used Tweet- Motif to deﬂate rumors, uncover scams, summarize sentiment, and track political protests in real-time. A demo of TweetMotif, plus its source code, is available at http://tweetmotif.com.
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Authors: Tumasjan Andranik , Sprenger Timm , Sandner Philipp , Welpe Isabell
Published: November 2022

Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Activity Lifespan: An Analysis of User Survival Patterns in Online Knowledge Sharing Communities
Authors: Yang Jiang , Wei Xiao , Ackerman Mark , Adamic Lada
Published: November 2022

Retaining participation is crucial for information services, online knowledge sharing services among them. We present the first comprehensive analysis of users’ activity lifespan across three predominant online knowledge-sharing communities. Extending previous work focusing on initial interactions of new users, we use survival analysis to quantify participation patterns that can be used to predict individual lifespan over the long term. We discuss how cross-site differences in user participation and the underlying factors can be related to differences in system design and culture. We conduct a longitudinal comparison of the communities’ evolvement between two distinct stages, the initial days just after the site launch and one year later. We also observe that sub-communities corresponding to different topics differ in their ability to sustain users. All results reveal the complexity and diversity in users’ engagement to a site and design implications are discussed.
Governance in Social Media: A Case Study of the Wikipedia Promotion Process
Authors: Leskovec Jure , Huttenlocher Daniel , Kleinberg Jon
Published: November 2022

Social media sites are often guided by a core group of committed users engaged in various forms of {governance. A crucial aspect of this type of governance is deliberation, in which such a group reaches decisions on issues of importance to the site. Despite its crucial — though subtle — role in how a number of prominent social media sites function, there has been relatively little investigation of the deliberative aspects of social media governance. Here we explore this issue, investigating a particular deliberative process that is extensive, public, and recorded: the promotion of Wikipedia admins, which is determined by elections that engage committed members of the Wikipedia community. We find that the group decision-making at the heart of this process exhibits several fundamental forms of relative assessment. First we observe that the chance that a voter will support a candidate is strongly dependent on the relationship between characteristics of the voter and the candidate. Second we investigate how both individual voter decisions and overall election outcomes can be based on models that take into account the sequential, public nature of the voting.
Characterizing Microblogs with Topic Models
Authors: Ramage Daniel , Dumais Susan , Liebling Dan
Published: November 2022

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.
Faceplant: Impression (Mis)management in Facebook Status Updates
Authors: Barash Vladimir , Ducheneaut Nicolas , Isaacs Ellen , Bellotti Victoria
Published: November 2022

While recent research examined the impressions projected by users of Social Network Sites through their relatively static online profiles, the addition of status updates to Facebook offers the opportunity to study a more fluid type of impression management. In this paper, we take a first look at data collected with a custom application designed to capture the impressions both “given” and “given off” by a user’s status updates. We show that while users generally succeed at presenting a positive image of themselves, they are only partially aware of how they are coming across and tend to underestimate the strength of the impressions they foster. This is particularly prevalent in the case of self-importance, giving credence to the notion that projecting an inflated sense of self can be a risk in a world where impressions are formed based on “micro updates.”
Finding Gold in Intranet Data: A Comparison of Mined and Surveyed Social Networks
Authors: Zhao Chen , Liu Baozhong
Published: November 2022

The goal of this research is to identify to what extent a social network extracted from public intranet data sources represents the actual interactions among people as reflected by a self-reported survey. We describe a case study we conducted within a large multinational software company comparing social networking information gathered from a traditional socio-centric network analysis (SNA) survey with various public intranet sources. Our results suggest that the value which automatically harvested social networks represent good percentage of actual ties with less privacy concern and low cost.
Social Intellisense: A Task-Embedded Interface to Folksonomies
Authors: Counts Scott , Fisher Kristie , Hoff Aaron
Published: November 2022

We present an application for accessing and creating socially constructed sets of information. Users store and retrieve information, such as bits of text, through the use of “keys” consisting of node names strung together using periods (e.g., “conferences.icwsm.dates”) that point to user-added content (“May 23-26, 2010”). Furthermore, nodes can point to services on the web (e.g., “facebook.me.status” retrieves the user’s current Facebook status). In short, Social Intellisense is a user interface mechanism for folksonomies that allows users to collaboratively “remap” web and user-added content via strings of their own creation. A study with our Social Intellisense-enabled email client showed that the paradigm is comparable or preferable to traditional web browser methods for retrieving certain information online. Issues and potential solutions to mismatches between a user’s task at hand and collaboratively organized information are discussed.
What’s Worthy of Comment? Content and Comment Volume in Political Blogs
Authors: Yano Tae , Smith Noah
Published: November 2022

In this paper we aim to model the relationship be- tween the text of a political blog post and the comment volume—that is, the total amount of response—that a post will receive. We seek to accurately identify which posts will attract a high-volume response, and also to gain insight about the community of readers and their interests. We design and evaluate variations on a latent- variable topic model that links text to comment volume.
TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief
Authors: Kumar Shamanth , Barbier Geoffrey , Abbasi Mohammad , Liu Huan
Published: November 2022

Social media is becoming popular as a key source of information on disasters and crisis situations. Humanitarian Aid and Disaster Relief (HADR) respondents can gain valuable insights and situational awareness by monitoring social mediabased feeds. Specifically, the use of microblogs (i.e., Twitter) has been shown to provide new information not otherwise attainable. In this paper, we present a new application designed to help HADR relief organizations to track, analyze, and monitor tweets. The purpose of this tool is to help these first responders gain situational awareness immediately after a disaster or crisis. The tool is capable of monitoring and analyzing location and keyword specific Tweets with nearreal- time trending, data reduction, historical review, and integrated data mining tools. In this paper, we discuss the utility of this tool through a case study on tweets related to the Cholera crisis in Haiti.
Event Detection in Twitter
Authors: Weng Jianshu , Lee Bu-Sung
Published: November 2022

Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on  detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering of Wavelet-based Signals). EDCoW builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words. It then filters away the trivial words by looking at their corresponding signal autocorrelations. The remaining words are then clustered to form events with a modularity-based graph partitioning technique. Experimental results show promising result of EDCoW.
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
Authors: Lee Kyumin , Eoff Brian , Caverlee James
Published: November 2022

The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers/following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification.
Diversity Measurement of Recommender Systems under Different User Choice Models
Authors: Szlávik Zoltán , Kowalczyk Wojtek , Schut Martijn
Published: November 2022

Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.
Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency
Authors: Verma Sudha , Vieweg Sarah , Corvey William , Palen Leysia , Martin James , Palmer Martha , Schram Aaron , Anderson Kenneth
Published: November 2022

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies.We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.
Political Polarization on Twitter
Authors: Conover Michael , Ratkiewicz Jacob , Francisco Matthew , Goncalves Bruno , Menczer Filippo , Flammini Alessandro
Published: November 2022

In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.
The Party Is Over Here: Structure and Content in the 2010 Election
Authors: Livne Avishay , Simmons Matthew , Adar Eytan , Adamic Lada
Published: November 2022

In this work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm (2010) elections in the U.S. Our data includes almost 700 candidates and over 690k documents that they produced and cited in the 3.5 years leading to the elections. We utilize graph and text mining techniques to analyze differences between Democrats, Republicans and Tea Party candidates, and suggest a novel use of language modeling for estimating content cohesiveness. Our findings show significant differences in the usage patterns of social media, and suggest conservative candidates used this medium more effectively, conveying a coherent message and maintaining a dense graph of connections. Despite the lack of party leadership, we find Tea Party members display both structural and language-based cohesiveness. Finally, we investigate the relation between network structure, content and election results by creating a proof-of-concept model that predicts candidate victory with an accuracy of 88.0%.
Extracting Meta Statements from the Blogosphere
Authors: Mesquita Filipe , Barbosa Denilson
Published: November 2022

Information extraction systems have been recently proposed for organizing and exploring content in large online text corpora as information networks. In such networks, the nodes are named entities (e.g., people, organizations) while the edges correspond to statements indicating relations among such entities. To date, such systems extract rather primitive networks, capturing only those relations which are expressed by direct statements. In many applications, it is useful to also extract more subtle relations which are often expressed as meta statements in the text. These can, for instance provide the context for a statement (e.g., “Google acquired YouTube on October 2006”), or repercussion about a statement (e.g., “The US condemned Russia’s invasion of Georgia”). In this work, we report on a system for extracting relations expressed in both direct statements as well as in meta statements. We propose a method based on Conditional Random Fields that explores syntactic features to extract both kinds of statements seamlessly. We follow the Open Information Extraction paradigm, where a classifier is trained to recognize any type of relation instead of specific ones. Finally, our results show substantial improvements over a state-of-the-art information extraction system, both in terms of accuracy and, especially, recall.
RT to Win! Predicting Message Propagation in Twitter
Authors: Petrovic Sasa , Osborne Miles , Lavrenko Victor
Published: November 2022

Twitter is a very popular way for people to share information on a bewildering multitude of topics. Tweets are propagated using a variety of channels: by following users or lists, by searching or by retweeting. Of these vectors, retweeting is arguably the most effective, as it can potentially reach the most people, given its viral nature. A key task is predicting if a tweet will be retweeted, and solving this problem furthers our understanding of message propagation within large user communities. We carry out a human experiment on the task of deciding whether a tweet will be retweeted which shows that the task is possible, as human performance levels are much above chance. Using a machine learning approach based on the passive-aggressive algorithm, we are able to automatically predict retweets as well as humans. Analyzing the learned model, we find that performance is dominated by social features, but that tweet features add a substantial boost.
An Optimized Web Feed Aggregation Approach for Generic Feed Types
Authors: Urbansky David , Reichert Sandro , Muthmann Klemens , Schuster Daniel , Schill Alexander
Published: November 2022

Web feeds are a popular way to access updates for contentin the World Wide Web. Unfortunately, the technology be-hind web feeds is based on polling. Thus, clients ask the feedserver regularly for updates. There are two concurrent prob-lems with this approach. First, many times a client asks forupdates, there is no new item and second, if the client’s up-date interval is too large it might be notiﬁed too late or evenmiss items. In this work we present adaptive feed polling algorithms. Thealgorithms learn from the previous behaviors of feeds andpredict their future behaviors. To evaluate these algorithmswe created a real set of over 180,000 diversiﬁed feeds andcollected a dataset of their updates for a time of three weeks.We tested our adaptive algorithms on this set and show thatadaptive feed polling reduces trafﬁc signiﬁcantly and pro-vides near-real-time updates.
High Correlation between Incoming and Outgoing Activity: A Distinctive Property of Online Social Networks?
Authors: Saez-Trumper Diego , Nettleton David , Baeza-Yates Ricardo
Published: November 2022

User influence is an important topic of research for online social networks. Recent work has shown that a user's influence is not directly related with node in-degree. However, the definition of what is an influential or relevant user is still an open subject. In general, we can say that an influential user has the ability to produce incoming activity in an interaction graph. For this reason we have focused our attention on the user's incoming activity and the search for which factors are related with this indicator. We have studied a Facebook dataset and have found that outgoing activity is highly correlated with incoming activity. This characteristic is valid not only for users with a low level of activity, but it is also valid for high activity users. This result hasn't been reported before and appears to be an important factor of user behavior. To contrast this finding, we have compared it against a popular e-mail dataset and a Twitter dataset. The result was that we found a similar behavior for the Twitter Dataset, but for the e-mail dataset the correlation was lower. Hence, we conjecture that the high correlation may be a distinctive property of social networks. In our future work, we propose to extend this study to other social network platforms.
Prominence Ranking in Graphs with Community Structure
Authors: Adali Sibel , Lu Xiaohui , Magdon-Ismail Malik , Purnell Jonathan
Published: November 2022

We consider prominence ranking in graphs involving actors, their artifacts and the artifact groups. When multiple actors contributing to an artifact constitutes a social tie, associations between the artifacts can be used to infer prominence among actors. This is because prominent actors will tend to collaborate on prominent artifacts, and prominent artifacts will be associated with other prominent artifacts. Our testbed example is the DBLP co-authorship graph: multiple authors (the actors) collaborate to publish research papers (the artifacts); collaboration is the social tie. Papers have prominence themselves (eg. quality and impact of the work) and the prominence of the venues are tied to the prominence of the papers in them. We use our methods to infer prominence based on the venue-based associations of papers, and compare our rankings with external citation based measures of prominence. We compare with numerous other ranking algorithms, and show that the ranking performance gain from using the venues is statistically significant. What if there are no natural artifact groups like venues? We develop a new algorithm which uses discovered artifact groups. Our approach consists of two steps. First, we find artifact groups by linking artifacts with common contributors. Note that instead of finding communities of actors, we consider communities of artifacts. We then use these grouped artifacts in the prominence ranking algorithm. We consider different methods for obtaining the artifact groups, in particular a very efficient embedding based algorithm for graph clustering and show the effectiveness of our method in improving the ranking of actors. The inferred groups are as good as or better than the natural conference venues for DBLP.
Summarizing User-Contributed Comments
Authors: Khabiri Elham , Caverlee James , Hsu Chiao-Fang
Published: November 2022

User-contributed comments are one of the hallmarks of the Social Web, widely adopted across social media sites and mainstream news providers alike. While comments encourage higher-levels of user engagement with online media, their wide success places new burdens on users to process and assimilate the perspectives of a huge number of user-contributed perspectives. Toward overcoming this problem we study in this paper the comment summarization problem: for a set of n user-contributed comments associated with an online resource, select the best top-k comments for summarization. In this paper we propose (i) a clustering-based approach for identifying correlated groups of comments; and (ii) a precedence-based ranking framework for automatically selecting informative user-contributed comments. We find that in combination, these two salient features yield promising results.
Does Bad News Go Away Faster?
Authors: Wu Shaomei , Tan Chenhao , Kleinberg Jon , Macy Michael
Published: November 2022

We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.
Measuring User Influence on Twitter Using Modified K-Shell Decomposition
Authors: Brown Phil , Feng Junlan
Published: November 2022

Social influence can be described as power - the ability of a person to influence the thoughts or actions of others. Identifying influential users on online social networks such as Twitter has been actively studied recently. In this paper, we investigate a modified k-shell decomposition algorithm for computing user influence on Twitter. The input to this algorithm is the connection graph between users as defined by the follower relationship. User influence is measured by the k-shell level, which is the output of the k-shell decomposition algorithm. Our first insight is to modify this k-shell decomposition to assign logarithmic k-shell values to users, producing a measure of users that is surprisingly well distributed in a bell curve. Our second insight is to identify and remove peering relationships from the network to further differentiate users. In this paper, we include findings from our study.
Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks
Authors: Noulas Anastasios , Scellato Salvatore , Mascolo Cecilia , Pontil Massimiliano
Published: November 2022

Location-Based Social Networks (LBSN) present so far the most vivid realization of the convergence of the physical and virtual social planes. In this work we propose a novel approach on modeling human activity and geographical areas by means of place categories. We apply a spectral clustering algorithm on areas and users of two metropolitan cities on a dataset sourced from the most vibrant LBSN, Foursquare. Our methodology allows the identification of user communities that visit similar categories of places and the comparison of urban neighborhoods within and across cities. We demonstrate how semantic information attached to places could be plausibly used as a modeling interface for applications such as recommender systems and digital tourist guides.
ICWSM Sponsors
Authors: Breslin John
Published: November 2022

List of sponsors of the 2012 ICWSM conference.
Automatic Versus Human Navigation in Information Networks
Authors: West Robert , Leskovec Jure
Published: November 2022

People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that real-world networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network.
Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter
Authors: Mejova Yelena , Srinivasan Padmini
Published: November 2022

Most sentiment analysis studies address classification of a single source of data such as reviews or blog posts. However, the multitude of social media sources available for text analysis lends itself naturally to domain adaptation. In this study, we create a dataset spanning three social media sources -- blogs, reviews, and Twitter -- and a set of 37 common topics. We first examine sentiments expressed in these three sources while controlling for the change in topic. Then using this multi-dimensional data we show that when classifying documents in one source (a target source), models trained on other sources of data can be as good as or even better than those trained on the target data. That is, we show that models trained on some social media sources are generalizable to others. All source adaptation models we implement show reviews and Twitter to be the best sources of training data. It is especially useful to know that models trained on Twitter data are generalizable, since, unlike reviews, Twitter is more topically diverse.
Virality and Susceptibility in Information Diffusions
Authors: Hoang Tuan-Anh , Lim Ee-Peng
Published: November 2022

Viral diffusion allows a piece of information to widely and quickly spread within the network of users through word-ofmouth. In this paper, we study the problem of modeling both item and user factors that contribute to viral diffusion in Twitter network. We identify three behaviorial factors, namely user virality, user susceptibility and item virality, that contribute to viral diffusion. Instead of modeling these factors independently as done in previous research, we propose a model that measures all the factors simultaneously considering their mutual dependencies. The model has been evaluated on both synthetic and real datasets. The experiments show that our model outperforms the existing ones for synthetic data with ground truth labels. Our model also performs well for predicting the hashtags that have higher retweet likelihood. We finally present case examples that illustrate how the models differ from one another.
Defense Mechanism or Socialization Tactic? Improving Wikipedia’s Notifications to Rejected Contributors
Authors: Geiger R. , Halfaker Aaron , Pinchuk Maryana , Walling Steven
Published: November 2022

Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.
Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis
Authors: Park Jaram , Cha Meeyoung , Kim Hoh , Jeong Jaeseung
Published: November 2022

Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.
Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media
Authors: De Choudhury Munmun , Counts Scott , Gamon Michael
Published: November 2022

Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. As social media tools are increasingly used by individuals to broadcast their day-to-day happenings, or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better interpret and make sense of the behavior of millions of individuals. Motivated by literature in psychology, we study a popular representation of human mood landscape, known as the ‘circumplex model’ that characterizes affective experience through two dimensions: valence and activation. We identify more than 200 moods frequent on Twitter, through mechanical turk studies and psychology literature sources, and report on four aspects of mood expression: the relationship between (1) moods and usage levels, including linguistic diversity of shared content (2) moods and the social ties individuals form, (3) moods and amount of network activity of individuals, and (4) moods and participatory patterns of individuals such as link sharing and conversational engagement. Our results provide at-scale naturalistic assessments and extensions of existing conceptualizations of human mood in social media contexts.
Geographic Dissection of the Twitter Network
Authors: Kulshrestha Juhi , Kooti Farshad , Nikravesh Ashkan , Gummadi Krishna
Published: November 2022

Geography plays an important role in shaping societal interactions in the offline world. However, as more and more social interactions occur online via social net- working sites like Twitter and Facebook, users can in- teract with others unconstrained by their geolocations, raising the question: does offline geography still matter in online social networks? In this paper, we attempt to address this question by dissecting the Twitter so- cial network based on users’ geolocations and investi- gating how users’ geolocation impacts their participa- tion in Twitter, including their connections to others and the information they exchange with them. Our in-depth analysis reveals that geography continues to have a sig- nificant impact on user interactions in the Twitter social network. The influence of geography could be poten- tially explained by the shared national, linguistic, and cultural backgrounds of users from the same geographic neighborhood.
Transductive Learning for Real-Time Twitter Search
Authors: Zhang Xin , He Ben , Luo Tiejian
Published: November 2022

Recency is an important dimension of relevance for real-time Twitter search as users tend to be interested in fresh news and events. By incorporating various sources of evidence, the application of learning to rank (LTR) algorithms to real-time Twitter search has shown beneficial in finding not only relevant, but also recent tweets in response to given queries. However, the potential effectiveness brought by LTR may not have been fully exploited due to the lack of labeled data available for properly learning a ranking model, since human labels are expensive in real-world applications. To this end, this paper proposes a transductive algorithm that incrementally aggregate the labeled tweets through an iterative process. Experimental results on the standard Tweets11 dataset show that our approach is able to outperform strong baselines without the use of human labels.
Creating Stories: Social Curation of Twitter Messages
Authors: Duh Kevin , Hirao Tsutomu , Kimura Akisato , Ishiguro Katsuhiko , Iwata Tomoharu , Au Yeung Ching-Man
Published: November 2022

Social media has become ubiquitous. Tweets and other user-generated content have become so abundant that better tools for information organization are needed in order to fully exploit their potential richness. ”Social cu- ration” has recently emerged as a promising new frame- work for organizing and adding value to social media, complementing the traditional methods of algorithmic search and aggregation. For example, web services like Togetter and Storify empower users to collect and or- ganize tweets to form stories that are pertinent, mem- orable, and easy to read. While social curation services are gaining popularity, little academic research has stud- ied the phenomenon. In this work, we perform one of the first analysis of a large corpus of social curation data. We seek to understand why and how people cu- rate tweets. We also propose an machine learning sys- tem that suggests new tweets, increasing the curator’s productivity and breadth of perspective.
Where Is This Tweet From? Inferring Home Locations of Twitter Users
Authors: Mahmud Jalal , Nichols Jeffrey , Drews Clemens
Published: November 2022

We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classification approach can improve prediction accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the location of Twitter users.
Trust Propagation with Mixed-Effects Models
Authors: Overgoor Jan , Wulczyn Ellery , Potts Christopher
Published: November 2022

Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between strangers from trust ratings between acquaintances and the structure of the network that connects them. We investigate a trust propagation algorithm that is based on user triads where the trust one user has in another is predicted based on an intermediary user. The propagation function can be applied iteratively to propagate trust along paths between a source user and a target user. We evaluate this approach using the trust network of the CouchSurfing community, which consists of 7.6M trust-valued edges between 1.1M users. We show that our model out-performs one that relies only on the trustworthiness of the target user (a kind of public trust system). In addition, we show that performance is significantly improved by bringing in user-level variability using mixed-effects regression models.
Global Dynamics of Online Group Conversations
Authors: Bhatt Rushi , Barman Kishor
Published: November 2022

Public online groups allow individuals to carry out conver- sations of common interests. Study of such group conversa- tions provides a unique opportunity to study patterns of hu- man conversations without violating individual privacy. The observational studies conducted in this paper are an attempt to identify the main correlates of continued growth of con- versations, thereby clearing the path to developing predictive models user participation. We study temporal evolution of online group discussions. Surprisingly, we find that individual discussion groups dis- play distinctively q-exponential shaped inter-message times to reply distributions, unlike the power law distributions seen in email conversations. We show, using simulations, that the heavy-tailed distribution of time to reply, which we also ob- serve when all data is combined, originate from mixtures of q-exponentials. We also find that popular threads come to be so from the very beginning as opposed to evolving to be more popular as they grow. This raises new possibilities for devel- oping generative models of thread growth.