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125
Microblog Credibility Perceptions: Comparing the United States and China
"... Microblogs have become an increasingly important source of information, both in the U.S. (Twitter) and in China (Weibo). However, the brevity of microblog updates, combined with increasing access of microblog content through search rather than through direct network connections, makes it challenging ..."
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Microblogs have become an increasingly important source of information, both in the U.S. (Twitter) and in China (Weibo). However, the brevity of microblog updates, combined with increasing access of microblog content through search rather than through direct network connections, makes it challenging to assess the credibility of news relayed in this manner [34]. This paper reports on experimental and survey data that compare the impact of several features of microblog updates (author’s gender, name style, profile image, location, and degree of network overlap with the reader) on credibility perceptions among U.S. and Chinese audiences. We reveal the complex mechanism of credibility perceptions, identify several key differences in how users from each country critically consume microblog content, and discuss how to incorporate these findings into the design of improved user interfaces for accessing microblogs in different cultural settings.
Generating Event Storylines from Microblogs
"... Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on realtime events. ..."
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Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on realtime events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.
A.: Tweets beget propinquity: Detecting highly interactive communities on twitter using tweeting links
- In: WI ’12: Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence
, 2012
"... Abstract—Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many m ..."
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Abstract—Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users. I.
Epidemiological Modeling of News and Rumors on Twitter
"... Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to char ..."
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Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
Understanding Information Credibility on Twitter
"... Abstract—Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibilit ..."
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Abstract—Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different studies and generalize from their findings. In this paper, we argue for a methodology for making such studies more useful to the research community. First, the underlying ground truth values of credibility must be reliable. The specific constructs used to define credibility must be carefully defined. Secondly, the underlying network context must be quantified and documented. To illustrate these two points, we conduct a unique credibility study of two different data sets on the same topic, but with different network characteristics. We also conduct two different user surveys, and construct two additional indicators of credibility based on retweet behavior. Through a detailed statistical study, we first show that survey based methods can be extremely noisy and results may vary greatly from survey to survey. However, by combining such methods with retweet behavior, we can incorporate two signals that are noisy but uncorrelated, resulting in ground truth measures that can be predicted with high accuracy and are stable across different data sets and survey methods. Newsworthiness of tweets can be a useful frame for specific applications, but it is not necessary for achieving reliable credibility ground truth measurements. I.
Non-Parametric Scan Statistics for Event Detection and Forecasting in Heterogeneous Social Media Graphs
"... Event detection in social media is an important but challeng-ing problem. Most existing approaches are based on burst detection, topic modeling, or clustering techniques, which cannot naturally model the implicit heterogeneous network structure in social media. As a result, only limited informa-tion ..."
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Event detection in social media is an important but challeng-ing problem. Most existing approaches are based on burst detection, topic modeling, or clustering techniques, which cannot naturally model the implicit heterogeneous network structure in social media. As a result, only limited informa-tion, such as terms and geographic locations, can be used. This paper presents Non-Parametric Heterogeneous Graph Scan (NPHGS), a new approach that considers the entire heterogeneous network for event detection: we first model the network as a“sensor”network, in which each node senses its “neighborhood environment ” and reports an empirical p-value measuring its current level of anomalousness for each time interval (e.g., hour or day). Then, we efficiently max-imize a nonparametric scan statistic over connected sub-graphs to identify the most anomalous network clusters. Fi-nally, the event represented by each cluster is summarized with information such as type of event, geographical loca-tions, time, and participants. As a case study, we consider two applications using Twitter data, civil unrest event detec-tion and rare disease outbreak detection, and present empir-ical evaluations illustrating the effectiveness and efficiency of our proposed approach.
Integrating On-demand Fact-checking with Public Dialogue
"... Public dialogue plays a key role in democratic society. Such dialogue often contains factual claims, but participants and readers are left wondering what to believe, particularly when contributions to such dialogue come from a broad spectrum of the public. We explore the design space for introduc-in ..."
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Public dialogue plays a key role in democratic society. Such dialogue often contains factual claims, but participants and readers are left wondering what to believe, particularly when contributions to such dialogue come from a broad spectrum of the public. We explore the design space for introduc-ing authoritative information into public dialogue, with the goal of supporting constructive rather than confrontational discourse. We also present a specific design and realization of an archetypal sociotechnical system of this kind, namely an on-demand fact-checking service integrated into a crowd-sourced voters guide powered by deliberating citizens. The fact-checking service was co-designed with and staffed by professional librarians. Our evaluation examines the service from the perspectives of both users and librarians.
Extraction of Professional Interests from Social Web Profiles
"... Abstract. Many people share and communicate their private thoughts and opinions via systems like Facebook and Twitter. In this paper, we analyze if also professional interests of a user can be extracted from these activities and be distinguished from private interests. The results indicate that perf ..."
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Abstract. Many people share and communicate their private thoughts and opinions via systems like Facebook and Twitter. In this paper, we analyze if also professional interests of a user can be extracted from these activities and be distinguished from private interests. The results indicate that performance largely depends on the size and quality of the Social Web profiles. Methods for reducing noise and chatter for-high volume profiles improve quality, but reduce diversity of the profiles. 1
H.: Semantic expansion of hashtags for enhanced event detection in Twitter
- In: Proceedings of VLDB 2012 Workshop on Online Social Systems. (2012
"... In this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and e ..."
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In this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and event detection performance in comparison to word-based vector generation methods. By analyzing the contexts of hashtags and their co-occurrence statistics with other words, we identify their paradigmatic relationships and similarities. We make use of this information while applying a lexico-semantic expansion on tweet contents before clustering the tweets based on their similarities. Our aim is to tolerate spelling errors and capture statements which actually refer to the same concepts. We evaluate our enhancement solution on a three-day dataset of tweets with Turkish content. In our evaluations, we observe clearer clusters, improvements in accuracy, and earlier event detection times.
