We propose a new method of recommending questions to answerers so as to suit the answerers’ knowledge and interests in User-Interactive Question Answering (QA) communities. A question recommender can help answerers select the questions that interest them. This increases the number of answers, which will activate QA communities. An effective question recommender should satisfy the following three requirements: First, its accuracy should be higher than the existing category-based approach; more than 50% of answerers select the questions to answer according a fixed system of categories. Second, it should be able to recommend unanswered questions because more than 2,000 questions are posted every day. Third, it should be able to support even those people who have never answered a question previously, because more than 50% of users in current QA communities have never given any answer. To achieve an effective question recommender, we use question histories as well as the answer histories of each user by combining collaborative filtering schemes and content-base filtering schemes. Experiments on real log data sets of a famous Japanese QA community, Oshiete goo, show that our recommender satisfies the three requirements.

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.

In this paper we examine the effects of noise when creating a real-world weblog corpus for information retrieval. We focus on the DiffPost (Lee et al. 2008) approach to noise removal from blog pages, examining the difficulties encountered when crawling the blogosphere during the creation of a real-world corpus of blog pages. We introduce and evaluate a number of enhancements to the original DiffPost approach in order to increase the robustness of the algorithm. We then extend DiffPost by looking at the anchor-text to text ratio, and discover that the time-interval between crawls is more important to the successful application of noise-removal algorithms within the blog context, than any additional improvements to the removal algorithm itself.

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semi-supervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.

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.

Social media sites derive their value by providing a popular and dependable community for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure long-term success, we present a prototype system for automatically detecting and profiling destructive users in social media. We described the architecture of the system - inspired by the "broken windows" theory embraced by law enforcement - the results and insights gained from a preliminary study conducted to determine the efficacy of our approach, and a discussion of our ongoing research.

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

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r.t. features which may be easily removed by spammers.

This paper discusses a research based on analyzing criminal sentences on criminal trials on organized crime activity in Sicily pronounced from 2000 through 2006. Large criminal sentences related dataset collection activity in Italy is severely constrained for various reasons such as difficulty of data collection at the courthouses, unavailability of data in digital format, and classification criteria used in the public archives. Thus, in general, judicial statistics suffer from lack of reliability and informativeness. The objective of this research is to analyze the text of criminal sentences in a revisable and verifiable way, so that information is extracted on the trial leading to the sentence, the socio-economic environment in which the relevant events occurred, and the differences between the various districts conducting the trials. The purpose is to elaborate a tool of automated analysis of the text of the sentences that is generalizable to other areas of jurisprudence, and, outside of jurisprudence, to other temporal and geographical contexts. The 726 criminal sentences that have been converted into text files have been pronounced at all judicial levels in the four Sicilian districts for mafia-related crimes. This research is relevant because, for the first time in Italy, we aim to empirically describe the juridical response to the phenomenon of organized crime, by using a large and extendable database of criminal sentences that can be analyzed with data mining techniques, rather than deriving general conclusions from a focused small set of sentences.

Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion. We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%.