Fact Checking in Community Forums

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering; Neural Networks; Distributed Representation

Escaping the Natural Attitude About Gender

Alex Byrne’s article, “Are Women Adult Human Females?”, asks a question that Byrne treats as nearly rhetorical. Byrne’s answer is, ‘clearly, yes’. Moreover, Byrne claims, 'woman' is a biological category that does not admit of any interpretation as (also) a social category. It is important to respond to Byrne’s argument, but mostly because it is paradigmatic of a wider phenomenon. The slogan “women are adult human females” is a political slogan championed by anti-trans activists, appearing on billboards, pamphlets, and anti-trans online forums. In this paper, I respond to Byrne’s argument, revealing significant problems with its background assumptions, content, and methodology

The Best Answers? Think Twice: Online Detection of Commercial Campaigns in the CQA Forums

In an emerging trend, more and more Internet users search for information from Community Question and Answer (CQA) websites, as interactive communication in such websites provides users with a rare feeling of trust. More often than not, end users look for instant help when they browse the CQA websites for the best answers. Hence, it is imperative that they should be warned of any potential commercial campaigns hidden behind the answers. However, existing research focuses more on the quality of answers and does not meet the above need. In this paper, we develop a system that automatically analyzes the hidden patterns of commercial spam and raises alarms instantaneously to end users whenever a potential commercial campaign is detected. Our detection method integrates semantic analysis and posters' track records and utilizes the special features of CQA websites largely different from those in other types of forums such as microblogs or news reports. Our system is adaptive and accommodates new evidence uncovered by the detection algorithms over time. Validated with real-world trace data from a popular Chinese CQA website over a period of three months, our system shows great potential towards adaptive online detection of CQA spams.Comment: 9 pages, 10 figure

Feature analysis for web forum question post detection

A web forum which is also known as discussion board or Internet forum is an online community of users with a common interest. It is a problem-solving platform that engages experts across the globe. Both technical and non-technical problems are resolved on a daily basis within web forums. Research activities in this domain have been concentrated on answer detection with the assumption that the initial post of a thread is a question post. The quality of web forum question posts varies from excellent to mediocre or even spam. Detecting good question posts require utilization of salient features. In this paper, we implement a bag-of-words (BoW) model to mine web forum question posts. We empirically address the following questions in the paper. Can BoW model effectively detect web forum question post? What feature selection method is most appropriate for BoW model in this domain? Is choice of classifier influenced by web forum genre? We used three publicly available datasets of varying technical degrees for the experiments. The experimental results revealed that BoW can perform better than complex techniques that implement higher N-gram with part-of-speech tagging