Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83 .
Research in this area has investigated textual aspects of questions and answers. In so doing, it has proposed algorithmic solutions to automatically determine: the quality of questions @cite_8 @cite_22 and answers @cite_5 @cite_31 , the extent to which certain questions are easy to answer @cite_0 @cite_23 , and the type of a given question (e.g., factual or conversational) @cite_26 .
@cite_26 @cite_22 @cite_8 @cite_0 @cite_23 @cite_5 @cite_31
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Tens of thousands of questions are asked and answered every day on social question and answer (Q&A) Web sites such as Yahoo Answers. While these sites generate an enormous volume of searchable data, the problem of determining which questions and answers are archival quality has grown. One major component of this problem is the prevalence of conversational questions, identified both by Q&A sites and academic literature as questions that are intended simply to start discussion. For example, a conversational question such as "do you believe in evolution?" might successfully engage users in discussion, but probably will not yield a useful web page for users searching for information about evolution. Using data from three popular Q&A sites, we confirm that humans can reliably distinguish between these conversational questions and other informational questions, and present evidence that conversational questions typically have much lower potential archival value than informational questions. Further, we explore the use of machine learning techniques to automatically classify questions as conversational or informational, learning in the process about categorical, linguistic, and social differences between different question types. Our algorithms approach human performance, attaining 89.7 classification accuracy in our experiments. At community question answering services, users are usually encouraged to rate questions by votes. The questions with the most votes are then recommended and ranked on the top when users browse questions by category. As users are not obligated to rate questions, usually only a small proportion of questions eventually gets rating. Thus, in this paper, we are concerned with learning to recommend questions from user ratings of a limited size. To overcome the data sparsity, we propose to utilize questions without users rating as well. Further, as there exist certain noises within user ratings (the preference of some users expressed in their ratings diverges from that of the majority of users), we design a new algorithm called 'majority-based perceptron algorithm' which can avoid the influence of noisy instances by emphasizing its learning over data instances from the majority users. Experimental results from a large collection of real questions confirm the effectiveness of our proposals. Users tend to ask and answer questions in community question answering (CQA) services to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo! Answers data and the results demonstrate the effectiveness of our algorithm in distinguishing high-quality questions from low-quality ones. All askers who post questions in Community-based Question Answering (CQA) sites such as Yahoo! Answers, Quora or Baidu's Zhidao, expect to receive an answer, and are frustrated when their questions remain unanswered. We propose to provide a type of "heads up" to askers by predicting how many answers, if at all, they will get. Giving a preemptive warning to the asker at posting time should reduce the frustration effect and hopefully allow askers to rephrase their questions if needed. To the best of our knowledge, this is the first attempt to predict the actual number of answers, in addition to predicting whether the question will be answered or not. To this effect, we introduce a new prediction model, specifically tailored to hierarchically structured CQA sites.We conducted extensive experiments on a large corpus comprising 1 year of answering activity on Yahoo! Answers, as opposed to a single day in previous studies. These experiments show that the F1 we achieved is 24 better than in previous work, mostly due the structure built into the novel model. Synchronous social Q&A systems exist on the Web and in the enterprise to connect people with questions to people with answers in real-time. In such systems, askers' desire for quick answers is in tension with costs associated with interrupting numerous candidate answerers per question. Supporting users of synchronous social Q&A systems at various points in the question lifecycle (from conception to answer) helps askers make informed decisions about the likelihood of question success and helps answerers face fewer interruptions. For example, predicting that a question will not be well answered may lead the asker to rephrase or retract the question. Similarly, predicting that an answer is not forthcoming during the dialog can prompt system behaviors such as finding other answerers to join the conversation. As another example, predictions of asker satisfaction can be assigned to completed conversations and used for later retrieval. In this paper, we use data from an instant-messaging-based synchronous social Q&A service deployed to an online community of over two thousand users to study the prediction of: (i) whether a question will be answered, (ii) the number of candidate answerers that the question will be sent to, and (iii) whether the asker will be satisfied by the answer received. Predictions are made at many points of the question lifecycle (e.g., when the question is entered, when the answerer is located, halfway through the asker-answerer dialog, etc.). The findings from our study show that we can learn capable models for these tasks using a broad range of features derived from user profiles, system interactions, question setting, and the dialog between asker and answerer. Our research can lead to more sophisticated and more useful real-time Q&A support. Question answering (QA) helps one go beyond traditional keywords-based querying and retrieve information in more precise form than given by a document or a list of documents. Several community-based QA (CQA) services have emerged allowing information seekers pose their information need as questions and receive answers from their fellow users. A question may receive multiple answers from multiple users and the asker or the community can choose the best answer. While the asker can thus indicate if he was satisfied with the information he received, there is no clear way of evaluating the quality of that information. We present a study to evaluate and predict the quality of an answer in a CQA setting. We chose Yahoo! Answers as such CQA service and selected a small set of questions, each with at least five answers. We asked Amazon Mechanical Turk workers to rate the quality of each answer for a given question based on 13 different criteria. Each answer was rated by five different workers. We then matched their assessments with the actual asker's rating of a given answer. We show that the quality criteria we used faithfully match with asker's perception of a quality answer. We furthered our investigation by extracting various features from questions, answers, and the users who posted them, and training a number of classifiers to select the best answer using those features. We demonstrate a high predictability of our trained models along with the relative merits of each of the features for such prediction. These models support our argument that in case of CQA, contextual information such as a user's profile, can be critical in evaluating and predicting content quality. The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans