bluuebunny/crossref_metadata_2025 huggingface dataset:
"I Can’t Keep It Up." A Dataset from the Defunct Voat.co News Aggregator
Authors: Mekacher Amin , Papasavva Antonis
Published: November 2022

Voat.co was a news aggregator website that shut down on December 25, 2020. The site had a troubled history and was known for hosting various banned subreddits. This paper presents a dataset with over 2.3M submissions and 16.2M comments posted from 113K users in 7.1K subverses (the equivalent of subreddit for Voat). Our dataset covers the whole lifetime of Voat, from its developing period starting on November 8, 2013, the day it was founded, April 2014, up until the day it shut down (December 25, 2020).

This work presents the largest and most complete publicly available Voat dataset, to the best of our knowledge. Along with the release of this dataset, we present a preliminary analysis covering posting activity and daily user and subverse registration on the platform so that researchers interested in our dataset can know what to expect.

Our data may prove helpful to false news dissemination studies as we analyze the links users share on the platform, finding that many communities rely on alternative news press, like Breitbart and GatewayPundit, for their daily discussions. In addition, we perform network analysis on user interactions finding that many users prefer not to interact with subverses outside their narrative interests, which could be helpful to researchers focusing on polarization and echo chambers. Also, since Voat was one of the platforms banned Reddit communities migrated to, we are confident our dataset will motivate and assist researchers studying deplatforming. Finally, many hateful and conspiratorial communities were very popular on Voat, which makes our work valuable for researchers focusing on toxicity, conspiracy theories, cross-platform studies of social networks, and natural language processing.
“This Candle Has No Smell”: Detecting the Effect of COVID Anosmia on Amazon Reviews Using Bayesian Vector Autoregression
Authors: Beauchamp Nick
Published: November 2022

While there have been many efforts to monitor or predict Covid using digital traces such as social media, one of the most distinctive and diagnostically important symptoms of Covid -- anosmia, or loss of smell -- remains elusive due to the infrequency of discussions of smell online. It was recently hypothesized that an inadvertent indicator of this key symptom may be misplaced complaints in Amazon reviews that scented products such as candles have no smell. This paper presents a novel Bayesian vector autoregression model developed to test this hypothesis, finding that "no smell" reviews do indeed reflect changes in US Covid cases even when controlling for the seasonality of those reviews. A series of robustness checks suggests that this effect is also seen in perfume reviews, but did not hold for the flu prior to Covid. These results suggest that inadvertent digital traces may be an important tool for tracking epidemics.
Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild
Authors: Brandl Stephanie , Hershcovich Daniel , Søgaard Anders
Published: November 2022

We argue that we need to evaluate model interpretability methods 'in the wild', i.e., in situations where professionals make critical decisions, and models can potentially assist them. We present an in-the-wild evaluation of token attribution based on Deep Taylor Decomposition, with professional journalists performing reliability assessments. We find that using this method in conjunction with RoBERTa-Large, fine-tuned on the Gossip Corpus, led to faster and better human decision-making, as well as a more critical attitude toward news sources among the journalists. We present a comparison of human and model rationales, as well as a qualitative analysis of the journalists' experiences with machine-in-the-loop decision making.
Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory
Authors: Cascalheira Cory J. , Hamdi Shah Muhammad , Scheer Jillian R. , Saha Koustuv , Boubrahimi Soukaina Filali , Choudhury Munmun De
Published: November 2022

Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.e., identity-based stress arising from adverse social conditions). Given that minority stress is the leading framework for understanding health inequity among SGM people, researchers and clinicians need accurate methods to detect minority stress. Since social media fulfills important developmental, affiliative, and coping functions for SGM people, social media may be an ecologically valid channel for detecting minority stress. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying minority stress dis-closed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted in an average accuracy of 65%.
Cyberbullying Detection across Social Media Platforms via Platform-Aware Adversarial Encoding
Authors: Yi Peiling , Zubiaga Arkaitz
Published: November 2022

Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms has received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.
Thin Slices of Online Profile Attributes
Authors: Kristin Stecher Kristin Stecher , Counts Scott
Published: November 2022

People form consistent impressions of others given surprisingly little information. With the advent of social networks, impressions now may form online rather than in a face-to-face context. This research explores aspects of online impression formation and discusses the crucial role of user profiles in this process. By examining users' decisions in an experimentally controlled social network, we show that users need only a thin slice of profile information in order to form impressions of others online. Additionally, specific profile attributes are evaluated for their perceived utility (how much do users choose to view these attributes), predictiveness (how well they serve as a proxy for a full profile), and diagnosticity (their ability to help users choose between online profiles). Findings provide design suggestions for better profile displays when space is restricted.
Examining Privacy in Blogging from the Reader's Perspective
Authors: Baumer Eric , Sueyoshi Mark , Tomlinson Bill
Published: November 2022

As new forms of digital communication and social media are used and adopted by various communities, privacy becomes an increasingly more difficult and complex issue. While previous work has studied bloggers' practices with respect to negotiating privacy, little research has been done on the ways in which readers of blogs perceive privacy in blogs. This poster presents results from a qualitative study of blog readers, describing how privacy is connected with contextual appropriateness, personal content, and perceptions of online and offline identity. These results are compared to previous, blogger-oriented findings in order to work towards a more complete understanding of privacy related perceptions and practices in blogging.
The Psychology of Blogging Communities: Social Identities and Knowledge Transfer Across Work-Groups
Authors: Sippel Bryan , Brodt Susan
Published: November 2022

In this article, we propose that blog communities can enhance knowledge transfer in organization. They do so by shaping bloggers' group (subordinate) and organizational (superordinate) identities, which affect knowledge sharing between members of different work-groups in an organization. Building on existing research on psychological identity and on knowledge transfer, we make the case for the benefits of integrating these two research streams; more important, we identify blogging communities as factors in shaping individuals' identities and potentially facilitating knowledge transfer within organizations.
RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
Authors: Tsur Oren , Rappoport Ari
Published: November 2022

We present an algorithm for automatically ranking user-generated book reviews according to review helpfulness. Given a collection of reviews, our RevRank algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a "virtual core" review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. Our experiments show that RevRank clearly outperforms a baseline imitating the Amazon user vote review ranking system.
Sidelines: An Algorithm for Increasing Diversity in News and Opinion Aggregators
Authors: Munson Sean , Zhou Daniel , Resnick Paul
Published: November 2022

Aggregators rely on votes, and links to select and present subsets of the large quantity of news and opinion items generated each day. Opinion and topic diversity in the output sets can provide individual and societal benefits, but simply selecting the most popular items may not yield as much diversity as is present in the overall pool of votes and links. In this paper, we define three diversity metrics that address different dimensions of diversity: inclusion, non-alienation, and proportional representation. We then present the Sidelines algorithm – which temporarily suppresses a voter’s preferences after a preferred item has been selected – as one approach to increase the diversity of result sets. In comparison to collections of the most popular items, from user votes on Digg.com and links from a panel of political blogs, the Sidelines algorithm increased inclusion while decreasing alienation. For the blog links, a set with known political preferences, we also found that Sidelines improved proportional representation. In an online experiment using blog link data as votes, readers were more likely to find something challenging to their views in the Sidelines result sets. These findings can help build news and opinion aggregators that present users with a broader range of topics and opinions.
You Are Where You Edit: Locating Wikipedia Contributors through Edit Histories
Authors: Lieberman Michael , Lin Jimmy
Published: November 2022

Whether knowingly or otherwise, Wikipedia contributors reveal their interests and expertise through their contribution patterns. An analysis of Wikipedia edit histories shows that it is often possible to associate contributors with relatively small geographic regions, usually corresponding to where they were born or where they presently live. For many contributors, the geographic coordinates of pages they have edited are tightly clustered. Results suggest that a wealth of information about contributors can be gleaned from edit histories. This illustrates the efﬁcacy of data mining on large, publicly-available datasets and raises potential privacy concerns.
Spectrum: Retrieving Different Points of View from the Blogosphere
Authors: Liu Jiahui , Birnbaum Larry , Pardo Bryan
Published: November 2022

Blogs have become an important medium for people to publish opinions and ideas on the web. Bloggers with interest and expertise in specific domains (e.g., politics, or technology) often create and maintain blogs to publish news, opinions and ideas about those domains. In this paper, we present Spectrum, a novel blog search system that enables users to search for different points of view related to a topic from the blogosphere. Given a topic, Spectrum retrieves blog posts from bloggers with interests and expertise in various domains, enabling users to browse and compare the opinions related to different aspects of the topic. To identify bloggers in a domain category, we propose a two-layer classification model that predicts bloggers’ interests based on short snippets of posts by the blogger and posts citing the blogger. The model characterizes the recurrent interests of bloggers and the importance of the bloggers in the domain. Experiments were conducted on a list of bloggers collected from blog directories, with their snippets collected from Google Blog Search. Categorization of bloggers’ interests achieves precision of 88.4% and recall of 84.5% by micro-averaging over all the categories, outperforming a baseline algorithm which directly classifies the bloggers’ snippets. We further apply this multi-perspective blog search to explore the ecological relationship between news and blogs. The system aggregates recent popular news stories and then automatically aggregates different points of view about those news stories in the blogosphere.
Event Detection and Tracking in Social Streams
Authors: Sayyadi Hassan , Hurst Matthew , Maykov Alexey
Published: November 2022

Events and stories can be characterized by a set of descriptive, collocated keywords. Intuitively, documents describing the same event will contain  similar sets of keywords,  and the graph of keywords for a document collection will contain clusters individual events. In this paper we build a network of keywords based on their co-occurrence in documents. We propose and develop a new event detection algorithm which creates a keyword graph and uses community detection methods analogous to those used for social network analysis to discover and describe events. Constellations of keywords describing an event may be used to find related articles. We also use the proposed algorithm to analyze events and track stories in social streams.
Information Diffusion in Computer Science Citation Networks
Authors: Shi Xiaolin , Tseng Belle , Adamic Lada
Published: November 2022

The paper citation network is a traditional social medium for the exchange of ideas and knowledge. In this paper we examine information diffusion in citation networks by analyzing the correlations between various citation choices and the subsequent impact of the articles. We find that citing recent papers and papers within the same scholarly community garners a slightly larger number of citations on average. However, this correlation is weaker among well-cited papers implying that for high impact work citing within one's field is of lesser importance.
Search in Social Tagging Systems Using Ontological User Profiles
Authors: Shepitsen Andriy , Tomuro Noriko
Published: November 2022

In this paper we present a modified hierarchical agglomerative clustering algorithm for building tag ontologies for social tagging systems. The modified algorithm first uses a clustering algorithm called Domain Similarity Clustering By Committee (DSCBC) (Tomuro et al. 2007) to derive a set of tag committees. We apply DSCBC to the tags entered by the users of social tagging systems and derive (un-ambiguous) committees of tags.  Using the committees, a tag ontology is constructed in which an ambiguous tag is separated into multiple, disambiguated tags/nodes. Then a tag profile of a given user is matched against the ontology, and an ontological profile of the user is created.  Finally a preference vector is fed into the (modified) FolkRank algorithm (Hotho et al. 2006a), and the web resources ordered based on the user's preferences are returned.  We run our system on the data from two social tagging systems and compare the results with other algorithms. The results showed our algorithm achieved marked improvements over other algorithms.
Information Cascades in the Blogosphere: A Look Behind the Curtain
Authors: Papagelis Manos , Bansal Nilesh , Koudas Nick
Published: November 2022

With an increasing number of people that read, write and comment on blogs, the blogosphere has established itself as an essential medium of communication. A fundamental characteristic of the blogging activity is that bloggers often link to each other. The succession of linking behavior determines the way in which information propagates in the blogosphere, forming cascades. Analyzing cascades can be useful in various applications, such as providing insight of public opinion on various topics and developing better cascade models. This paper presents the results of an excessive study on cascading behavior in the blogosphere. Our objective is to present trends on the degree of engagement and reaction of bloggers in stories that become available in blogs under various parameters and constraints. To this end, we analyze cascades that are attributed to different population groups constrained by factors of gender, age, and continent. We also analyze how cascades differentiate depending on their subject. Our analysis is performed on one of the largest available datasets, including 30M active blogs and 700M posts. The study reveals large variations in the properties of cascades.
Finding Opinionated Blogs Using Statistical Classifiers and Lexical Features
Authors: Liu Feifan , Li Bin , Liu Yang
Published: November 2022

This paper systematically exploited various lexical features for opinion analysis on blog data using a statistical learning framework. Our experimental results using the TREC Blog track data show that all the features we explored effectively represent opinion expressions, and different classification strategies have a significant impact on opinion classification performance. We also present results when combining opinion analysis with the retrieval component for the task of retrieving relevant and opinionated blogs. Compared with the best results in the TREC evaluation, our system achieves reasonable performance, but does not rely on much human knowledge or deep level linguistic analysis.
A Comparative Analysis of Trust-Enhanced Recommenders for Controversial Items
Authors: Victor Patricia , Cornelis Chris , De Cock Martine , Teredesai Ankur
Published: November 2022

A particularly challenging task for recommender systems (RSs) is deciding whether to recommend an item that received a variety of high and low scores from its users. RSs that incorporate a trust network among their users have the potential to make more personalized recommendations for such controversial items (CIs) compared to collaborative filtering (CF) based systems, provided they succeed in utilizing the trust information to their advantage. In this paper, we formalize the concept of CIs in RSs. We then compare the performance of several well-known trust-enhanced techniques for effectively personalizing the recommendations for CIs versus random items in the RS. Furthermore, we introduce a new algorithm that maximizes the synergy between CF and its trust-based variants, and show that the new algorithm outperforms other trust-based techniques in generating rating predictions for CIs.
Compositionality Principle in Recognition of Fine-Grained Emotions from Text
Authors: Neviarouskaya Alena , Prendinger Helmut , Ishizuka Mitsuru
Published: November 2022

The recognition of personal emotional state or sentiment conveyed through text is the main task we address in our research. The communication of emotions through text messaging and posts of personal blogs poses the ‘informal style of writing’ challenge for researchers expecting grammatically correct input. Our Affect Analysis Model was designed to handle the informal messages written in an abbreviated or expressive manner. While constructing our rule-based approach to affect recognition from text, we followed the compositionality principle. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of personal blog posts.
Trust Incident Account Model: Preliminary Indicators for Trust Rhetoric and Trust or Distrust in Blogs
Authors: Rubin Victoria
Published: November 2022

This paper defines a concept of trust incident accounts as verbal reports of empirical episodes in which a trustor has reached a state of positive or negative expectations of a trustee’s behavior under associated risks. Such expectations are equated to trust and distrust, correspondingly, and present a sharp contrast with hypocritical use of trust rhetoric with ulterior motives such as an attempt to manipulate readers or gain trustworthiness. Distinguishing the three: trust, distrust, and trust rhetoric, is formulated as a new challenge in sentiment analysis and opinion-mining. Based on a preliminary exploration of trust narratives in blogs, 14 categories of textual indicators were identified manually. The finer-grain analytical model of trust incident accounts is proposed to include 12 information extraction frame components: trustor, trustee, source, textual clue, trust valence, risks, reasons, actions, trustor-trustee relationship, narrow context, broad domain, and complements. The study draws a cross-disciplinary theoretical bridge from social science and information technology trust literature to opinion-mining, and emphasizes the value of understanding trust in longer-term social relations.
A Web-Based System from Data Collection and Community Building
Authors: Zahn Ista
Published: November 2022

Your Psyche is a new web-based data collection system. This system was developed to take advantage of modern Internet technologies to create an engaging and rewarding experience for participants. Key to this effort is the development of a sophisticated and powerful feedback system that allows participants to see and understand the results of studies they have participated in. This feedback system not only allows participants to see how their results compare those of other participants, but also to see how the variables in the study relate to each other. The feedback system also allows participants to select specific comparison demographics: for example, a young female participant can see how her scores compare to those of other young females. Preliminary evidence is encouraging, but also suggests that participants are sometimes overwhelmed and confused by the sophistication of the feedback system. Additional work and innovation are needed to develop ways to present complex information in straightforward ways that lay persons can understand.
Microblogging Inside and Outside the Workplace
Authors: Ehrlich Kate , Shami N.
Published: November 2022

Microblogging has recently generated a lot of research interest. Yet very little is known about how corporate employees use microblogging tools. This study examined microblogging in the workplace by conducting a content analysis comparing posts from individuals who were using an internal proprietary tool and Twitter simultaneously. In both settings, posts that provided information or were directed to others were more common than posts on status. Within these categories, it was more frequent to provide information externally than internally but more common to ask questions either through broadcast or directed posts internally than externally. Qualitative interviews explored users’ motivations regarding microblogging behavior. The paper concludes with a discussion of the implications of microblogging for business use.
Photo Tagging Over Time: A Longitudinal Study of the Role of Attention, Network Density, and Motivations
Authors: Russo Paul , Nov Oded
Published: November 2022

Along with the growth in artifact sharing in online communities such as Flickr, YouTube, and Facebook comes the demand for adding descriptive meta-information, or tags. Tags help individuals to organize and communicate the content and context of their work for themselves and for others. This longitudinal study draws on research in social psychology, network theory and online communities to explain tagging over time. Our findings suggest that tagging increases as a contributor receives attention from others in the community. Further, we find that the more a user's network neighbors are connected to each other directly, the less the focal user will tend to tag his photos. However, density interacts with attention such that those who are surrounded by a dense ego network respond more to attention than others whose ego networks are sparsely interconnected. Unexpectedly, we find no direct correlation between tagging and the individual motivations of enjoyment and commitment. While commitment is not directly associated with tagging, there is an interaction effect such that the effect of commitment on tagging is positive for users with low-density ego networks and negative when a user is surrounded by a high-density network. Directions for future research as well as implications for theory and practice are discussed.
Sequential Influence Models in Social Networks
Authors: Cosley Dan , Huttenlocher Daniel , Kleinberg Jon , Lan Xiangyang , Suri Siddharth
Published: November 2022

The spread of influence among individuals in a social network can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between various models as well as to relate these models to actual social network data. Here we consider two of the most fundamental definitions of influence, one based on a small set of "snapshot'' observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. We study the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. We validate our analysis using the history of social interactions on Wikipedia; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of social influence.
The Perceived Credibility of Online Encyclopedias Among Children
Authors: Flanagin Andrew , Metzger Miriam
Published: November 2022

This study examined young people’s trust of Wikipedia as an information resource. A large-scale probability-based survey with embedded quasi-experiments was conducted with 2,747 children in the U.S. ranging from 11 to 18 years old. Results show that young people find Wikipedia to be fairly credible, but also exhibit an awareness of potential problems with non-expert, user-generated content in anonymous environments. Children tend to evaluate the credibility of online encyclopedia information with this in mind, at times with what appears to be an unwarranted devaluation of this information.
Co-Participation Networks Using Comment Information
Authors: Rangwala Huzefa , Jamali Salman
Published: November 2022

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. We use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We also compare network properties of our co-participation network to a previously defined reply-answer network on news forums.
Social Dynamics of Digg
Authors: Hogg Tad , Lerman Kristina
Published: November 2022

Online social media often highlight content that is highly rated by neighbors in a social network. For the news aggregator Digg, we use a stochastic model to distinguish the effect of the increased visibility from the network from how interesting content is to users. We find a wide range of interest, and distinguish stories primarily of interest to users in the network from those of more general interest to the user community. This distinction helps predict a story's eventual popularity from users' early reactions to the story.
A Ranking Based Model for Automatic Image Annotation in a Social Network
Authors: Denoyer Ludovic , Gallinari Patrick
Published: November 2022

We propose a relational ranking model for learning to tag images in social media sharing systems. This model learns to associate a ranked list of tags to unlabeled images, by considering simultaneously content information (visual or textual) and relational information among the images. It is able to handle implicit relations like content similarities, and explicit ones like friendship or authorship. The model itself is based on a transductive algorithm thats learns from both labeled and unlabeled data. Experiments on a real corpus extracted from Flickr show the effectiveness of this model.
Monetary Value of Customer Networks in Mobile Social Networking Services
Authors: Matsumura Naohiro , Yamamoto Hikaru
Published: November 2022

Mobile Social Networking Services (SNS) are an emerging trend in which individuals of similar interests communicate with one another using mobile phones. In this paper, we calculate the monetary value of customers and their networks in mobile SNS using the official data provided by a service provider. The mobile SNS enable users to create their avatars to communicate with each other via blog comments and communities. The company sells various items for these avatars such as apparel and interiors. This service generates consumer purchase history data and user network data. In previous research, the value of the customer is often estimated using the purchase history, but the network value is neglected. In the field of computer science, the value of the network is evaluated via the network structure, but not much attention is paid to the economic aspect of each node. The aim of this paper is to incorporate the social factor with the customer purchase database and calculate the monetary value of each customer and his or her network. The results of empirical analysis show that our approach is useful in finding valuable customers for marketing activities and outperforms conventional metrics such as degree centrality.
Why do Users Tag? Detecting Users’ Motivation for Tagging in Social Tagging Systems
Authors: Strohmaier Markus , Körner Christian , Kern Roman
Published: November 2022

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question: 1.) What motivates users to tag resources, and in what ways is user motivation amenable to quantitative analysis? 2.) Does users' motivation for tagging vary within and across social tagging systems, and if so how? and 3.) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply the measures to datasets from 8 different tagging systems. Our results show that a) users' motivation for tagging varies not only across, but also within tagging systems, and that b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (i) the development of tag recommenders, (ii) the analysis of tag semantics and (iii) the design of search algorithms for social tagging systems.
User Interest and Interaction Structure in Online Forums
Authors: Liu Di , Percival Daniel , Fienberg Stephen
Published: November 2022

We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.
Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia
Authors: Ciampaglia Giovanni Luca , Vancheri Alberto
Published: November 2022

We study the distribution of the activity period of users in five of the largest localized versions of the free, on- line encyclopedia Wikipedia. We find it to be consis- tent with a mixture of two truncated log-normal distri- butions. Using this model, the temporal evolution of these systems can be analyzed, showing that the statis- tical description is consistent over time.
Generating Domain-Specific Clues Using News Corpus for Sentiment Classification
Authors: Kim Youngho , Choi Yoonjung , Myaeng Sung-Hyon
Published: November 2022

This paper addresses the problem of automatically generating domain-specific sentiment clues. The main idea is to bootstrap from a small seed set and generate new clues by using dependencies and collocation information between sentiment clues and sentence-level topics that would be a primary subject of sentiment expression (e.g., event, company, and person). The experiments show that the aggregated clues are effective for sentiment classification.
Discovering Serendipitous Information from Wikipedia by Using Its Network Structure
Authors: Noda Yohei , Kiyota Yoji , Nakagawa Hiroshi
Published: November 2022

Many researchers conducted studies on extracting relevant information from web documents. However, there are few studies on extracting serendipitous information. We propose methods to discover unexpected information from Wikipedia by using its network structure, for example, the distance between two categories. We evaluated two methods: a classification-based method using support vector machines (SVMs), and a ranking-based method using regression. We demonstrate advantages of regression over classification.
Generate Adjective Sentiment Dictionary for Social Media Sentiment Analysis Using Constrained Nonnegative Matrix Factorization
Authors: Peng Wei , Park Dae Hoon
Published: November 2022

Although sentiment analysis has attracted a lot of research, little work has been done on social media data compared to product and movie reviews. This is due to the low accuracy that results from the more informal writing seen in social media data. Currently, most of sentiment analysis tools on social media choose the lexicon-based approach instead of the machine learning approach because the latter requires the huge challenge of obtaining enough human-labeled training data for extremely large-scale and diverse social opinion data. The lexicon-based approach requires a sentiment dictionary to determine opinion polarity. This dictionary can also provide useful features for any supervised learning method of the machine learning approach. However, many benchmark sentiment dictionaries do not cover the many informal and spoken words used in social media. In addition, they are not able to update frequently to include newly generated words online. In this paper, we present an automatic sentiment dictionary generation method, called Constrained Symmetric Nonnegative Matrix Factorization (CSNMF) algorithm, to assign polarity scores to each word in the dictionary, on a large social media corpus — digg.com. Moreover, we will demonstrate our study of Amazon Mechanical Turk (AMT) on social media word polarity, using both the human-labeled dictionaries from AMT and the General Inquirer Lexicon to compare our generated dictionary with. In our experiment, we show that combining links from both WordNet and the corpus to generate sentiment dictionaries does outperform using only one of them, and the words with higher sentiment scores yield better precision. Finally, we conducted a lexicon-based sentiment analysis on human-labeled social comments using our generated sentiment dictionary to show the effectiveness of our method.
What's in a @name? How Name Value Biases Judgment of Microblog Authors
Authors: Pal Aditya , Counts Scott
Published: November 2022

Bias can be defined as selective favoritism exhibited by human beings when posed with a task of decision making across multiple options. Online communities present plenty of decision making opportunities to their users. Users exhibit biases in their attachments, voting and ratings and other tasks of decision making. We study bias amongst microblog users due to the value of an author's name. We describe the relationship between name value bias and number of followers, and cluster authors and readers based on patterns of bias they receive and exhibit, respectively. For authors we show that content from known names (e.g., @CNN) is rated artificially high, while content from unknown names is rated artificially low. For readers, our results indicate that there are two types: slightly biased, heavily biased. A subsequent analysis of Twitter author names revealed attributes of names that underlie this bias, including effects for gender, type of name (individual versus organization), and degree of topical relevance. We discuss how our work can be instructive to content distributors and search engines in leveraging and presenting microblog content.
Identifying Representative Textual Sources in Blog Networks
Authors: Wade Karen , Greene Derek , Lee Conrad , Archambault Daniel , Cunningham Pádraig
Published: November 2022

We apply methods from social network analysis and visualization to facilitate a study of the Irish blogosphere from a cultural studies perspective.We focus on solving the practical issues that arise when the goal is to perform textual analysis of the corpus produced by a network of bloggers. Previous studies into blogging networks have noted difficulties arising when trying to identify the extent and boundaries of these networks. As a response to calls for increasingly data-led approaches in media and cultural studies, we discuss a variety of social network analysis methods that can be used to identify which blogs can be seen as members of a posited “Irish blogging network” (and hence sources of textual material). We identify hub blogs, communities of sites corresponding to different topics, and representative bloggers within these communities. Based on this study, we propose a set of guidelines for researchers who wish to map out blogging networks.
Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques
Authors: Reuter Timo , Cimiano Philipp , Drumond Lucas , Buza Krisztian , Schmidt-Thieme Lars
Published: November 2022

We tackle the problem of grouping content available in social media applications such as Flickr, Youtube, Panoramino etc. into clusters of documents describing the same event. This task has been referred to as event identiﬁcation before. We present a new formalization of the event identiﬁcation task as a record linkage problem and show that this formulation leads to a principled and highly efﬁcient solution to the problem. We present results on two datasets derived from Flickr — last.fm and upcoming — comparing the results in terms of Normalized Mutual Information and F-Measure with respect to several baselines, showing that a record linkage approach outperforms all baselines as well as a state-of-the-art system. We demonstrate that our approach can scale to large amounts of data, reducing the processing time considerably compared to a state-of-the-art approach. The scalability is achieved by applying an appropriate blocking strategy and relying on a Single Linkage clustering algorithm which avoids the exhaustive computation of pairwise similarities.
Taking It All In? Visual Attention in Microblog Consumption
Authors: Counts Scott , Fisher Kristie
Published: November 2022

Microblogging environments such as Twitter present a modality for interacting with information characterized by exposure to information “streams”. In this work, we examine what information in that stream is attended to, and how that attention corresponds to other aspects of microblog consumption and participation. To do this, we measured eye gaze, memory for content, interest ratings, and intended behavior of active Twitter users as they read their tweet steams. Our analyses focus on three sets of alignments: first, whether attention corresponds to other measures of user cognition such as memory (e.g., do people even remember what they attend to?); second, whether attention corresponds to behavior (e.g., are users likely to retweet content that is given the most attention); and third, whether attention corresponds to other attributes of the content and its presentation (e.g., do links attract attention?). We show a positive but imperfect alignment between user attention and other measures of user cognition like memory and interest, and between attention and behaviors like retweeting. To the third alignment, we show that the relationship between attention and attributes of tweets, such as whether it contains a link or is from a friend versus an organization, are complicated and in some cases counterintuitive. We discuss findings in relation to large scale phenomena like information diffusion and also suggest design directions to help maximize user attention in microblog environments
Technology-Mediated Citizen Science Participation: A Motivational Model
Authors: Nov Oded , Arazy Ofer , Anderson David
Published: November 2022

We propose and test a framework of the antecedents of contribution in two technology-mediated citizen science projects, with different degrees of task granularity. Comparing earlier findings on the motivations of volunteers in a web-based image analysis project (high granularity), with new findings on the motivations of volunteers in a volunteer computing project (low granularity), we found that participation task granularity is correlated with motivation levels. Further, we found that collective and intrinsic motives are the most salient motivational factors, whereas reward motives are less important for volunteers. Intrinsic, norm-oriented and reputation-seeking motives were most strongly associated with participation intentions, which were, in turn, associated with participation. Finally, comparing the relationship between motives and participation among the two volunteer populations, we found that active-participation volunteers are characterized by significantly stronger association between collective motives and contribution intention, whereas passive-participation volunteers are characterized by significantly stronger association between identification with the community and contribution intention. Implications for research and practice are discussed.
Exploiting User Interest on Social Media for Aggregating Diverse Data and Predicting Interest
Authors: Nori Nozomi , Bollegala Danushka , Ishizuka Mitsuru
Published: November 2022

More and more users have been taking various actions to diverse resources referred to by URLs such as news, web pages, images, products, movies as a result of the growth of social media. They are annotating, tweeting in Twitter, reblogging in Tumblr, and Liking in Facebook, etc. Analyses about these diverse actions will be useful for aggregating or integrating diverse resources. In this paper, we view users’ actions to resources as expressing their some interests, and by investigating how their interests are expressed in social media, we get suggestions for aggregations. Our results show that a certain kind of action (such as tagging on Delicious) can be used to make predictions on a different kind of action (such as favorite on Twitter). These analyses will be useful for aggregating or integrating diverse contents on multiple sources. In addition to some experimental analyses, we propose a novel method to predict users’ interests in social media, using time-evolving, multinomial relational data. Our experimental results show that the proposed method significantly outperforms standard tensor analysis and an existing state-of-the-art method (LDA) in prediction tasks.
Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs
Authors: Ahmad Muhammad , Keegan Brian , Williams Dmitri , Srivastava Jaideep , Contractor Noshir
Published: November 2022

Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theories of clandestine behavior.
Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing
Authors: Sharara Hossam , Rand William , Getoor Lise
Published: November 2022

Viral marketing mechanisms use the existing social network between customers to spread information about products and encourage product adoption. Existing viral marketing models focus on the dynamics of the diffusion process, however they typically: (a) only consider a single product campaign and (b) fail to model the evolution of the social network, as the trust between individuals changes over time, during the course of multiple campaigns. In this work, we propose an adaptive viral marketing model which captures: (1) multiple different product campaigns, (2) the diversity in customer preferences among different product categories, and (3) changing confidence in peers’ recommendations over time. By applying our model to a real-world network extracted from the Digg social news website, we provide insights into the effects of network dynamics on the different products’ adoption. Our experiments show that our proposed model outperforms earlier nonadaptive diffusion models in predicting future product adoptions. We also show how this model can be used to explore new viral marketing strategies that are more successful than classic strategies which ignore the dynamic nature of social networks.