Spam detection with a content-based random-walk algorithm

In this work we tackle the problem of the spam detection on the Web. Spam web pages have become a problem for Web search engines, due to the negative effects that this phe-nomenon can cause in their retrieval results. Our approach is based on a random-walk algorithm that obtains a ranking of pages according to their relevance and their spam likelihood. We introduce the novelty of taking into account the content of the web pages to characterize the web graph and to ob-tain an a- priori estimation of the spam likekihood of the web pages. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and the relations in the graph. Our experiments show that our proposed technique outperforms other link-based techniques for spam detection.Ministerio de Educación y Ciencia HUM2007-66607-C04-0

Gamification of community policing : SpamCombat

The purpose of this paper is two-fold. First, it seeks to introduce the conceptual prototype of SpamCombat, a Web application that helps combat spam through gamification of community policing. Second, it attempts to evaluate SpamCombat by identifying factors that can potentially drive users’ behavioral intention to adopt. A questionnaire seeking quantitative and qualitative responses was administered to 120 participants. The results indicate that behavioral intention to adopt SpamCombat is generally promising. Most participants appreciated the novelty of SpamCombat in supporting community policing to promote a spam-free cyber space. However, participants felt that using SpamCombat could be time-consuming

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin