Community-based question answering (CQA) platforms are crowd-sourced services for sharing user expertise on various topics, from mechanical repairs to parenting. While they naturally build-in an online social network infrastructure, they carry a very different purpose from Facebook-like social networks, where users "hang-out" with their friends and tend to share more personal information. It is unclear, thus, how the privacy concerns and their correlation with user behavior in an online social network translate into a CQA platform. This study analyzes one year of recorded traces from a mature CQA platform to understand the association between users' privacy concerns as manifested by their account settings and their activity in the platform. The results show that privacy preference is correlated with behavior in the community in terms of engagement, retention, accomplishments and deviance from the norm. We find privacy-concerned users have higher qualitative and quantitative contributions, show higher retention, report more abuses, have higher perception on answer quality and have larger social circles. However, at the same time, these users also exhibit more deviant behavior than the users with public profiles.
System perspective research develops techniques and tools to improve platform usability. It includes routing questions to expert users @cite_25 @cite_35 , extracting factual answers from QA archives @cite_24 and reusing the repository of past answers to answer new open questions @cite_31 . @cite_22 derive tips'' (a self-contained bit of non-obvious answer) from to address how-to'' queries. Social network perspective research attempts to understand the interplay between users' social connections and Q &A activities such as analyzing the social network of Quora @cite_8 , using social network properties and contribution behavior for content abusers detection @cite_16 .
@cite_35 @cite_22 @cite_8 @cite_24 @cite_31 @cite_16 @cite_25
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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. This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance. This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage "relational" evidences extracted from the Web corpus. In particular, as such an example, weadopt Web document co-occurrences, which can be interpreted as an "implicit" counterpart of Twitter follower relationships. Using both textual and relational features, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate to outperform the baseline approach. We evaluate our proposed system using real-life internetscale entity-relationship and social network graphs. Community based question answering platforms have attracted substantial users to share knowledge and learn from each other. As the rapid enlargement of CQA platforms, quantities of overlapped questions emerge, which makes users confounded to select a proper reference. It is urgent for us to take effective automated algorithms to reuse historical questions with corresponding answers. In this paper we focus on the problem with question retrieval, which aims to match historical questions that are relevant or semantically equivalent to resolve one s query directly. The challenges in this task are the lexical gaps between questions for the word ambiguity and word mismatch problem. Furthermore, limited words in queried sentences cause sparsity of word features. To alleviate these challenges, we propose a novel framework named HNIL which encodes not only the question contents but also the askers social interactions to enhance the question embedding performance. More specifically, we apply random walk based learning method with recurrent neural network to match the similarities between askers question and historical questions proposed by other users. Extensive experiments on a large scale dataset from a real world CQA site show that employing the heterogeneous social network information outperforms the other state of the art solutions in this task.