Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites

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5 There are also other measures to consider, e.g., the follower number and the times of being retweeted. In our experiments, we have tried these variants and found that no one is more effective than the above method. Relevance. Intuitively, a user is more likely to be an expert on an area that she is interested in. In the setting of Zhihu, a user tends to perform better on the topics that are more relevant to her interests. Status characteristic theory also conveys that task relevance is an important factor which affects one s performance (Oldmeadow et al., 2003). Weibo provides a good platform to infer users interests, which is helpful to derive relevance scores. We formulate relevance estimation as an information retrieval task. Let V denote a term vocabulary and w denote a word in V. Note that we take the union of the Weibo vocabulary and Zhihu vocabulary. The interest of a user u is modeled as a multinomial distribution over the terms in V, i.e., θ u = {θ u w} w V. Given a question q, we also model it as a multinomial distribution over the terms in V, i.e., θ q = {θ q w} w V. Following (Zhai, 2008), the relevance score between question q and user u can be estimated by the negative Kullback-Leibler divergence between θ q and θ u : Rel(q, u) = KL(θ q, θ u ) = w V p(w θ q ) log p(w θq ) p(w θ u ) (2) We first estimate θ q. The straightforward way is to estimate θ q based on the question text. However, the question text is usually short and noisy, which does not yield good results in our experiments. Recall a question is associated with a small set of user-annotated topic tags, and tags are good semantic indicators of the question. A topic tag usually indexes a considerable amount of questions, and we can use tags to leverage semantics from the indexed questions. Formally, we adopt the translation based model (Zhai, 2008) to estimate the question model: θ q w t q p(w, t q) = t q p(w t)p(t q) (3) where p(w t) is the translation probability from a tag to a term, and p(t q) is the empirical distribution of tag t in question q. Here we make an independent assumption: given a tag, the question is independent of a word, i.e., p(w t, q) = p(w t). The procedure can be interpreted as follows: sample a tag from the question and then compute the probability of translating the tag into a specific word. We estimate the tagterm translation probability as p(w t) = #(w,t)+1 w V #(w,t)+ V, where #(w, t) denotes the term frequency of w in the question text that tag t indexes. We use the additive-one smoothing. We also try to incorporate the question text into the above estimation formula. However, it does not result in any improvement. The main reason is that the question words may be too specific, as a comparison, tags provide a general level of semantics, which is more effective to identify expertise areas. Next, we estimate user interest model θ u. We consider aggregating all the tweets of a user as a document, and then estimate the document-term probability as θw u = denotes the term frequency of w in the aggregated document of user u. 4.2 Zhihu features #(w,u)+1 w V #(w,u)+ V, where #(w, u) Now we describe the features extracted from Zhihu, and we refer to them as baseline features since we take the performance of them as a base reference. We summarize these features in Table 2. These features have been extensively tested to be very effective by previous related studies (Song et al., 2010; Liu et al., 2011), which represent the state-of-art of the current task. Summary. We have considered two general types of knowledge in social media which are potential to improve user expertise estimation in Zhihu. It is easy to see that our approach can be equally applicable to other third-party websites which is text based and contain manually annotated tags. 660

8 History Window Size Systems NULL P+UniformG Rel question threads (B)aseline P+HisG B.+Weibo vs. B % +6.01% +3.05% 5 question threads (B)aseline P+HisG B.+Weibo vs. B % +8.13% +4.41% 10 question threads (B)aseline P+HisG B.+Weibo vs. B % +5.81% +3.73% Table 3: Overall ranking performance with varying history window sizes. *, **, *** indicate the improvement is significant at the level of 0.1, 0.05 and 0.01 respectively. Analysis of the effect of Weibo features. We now incorporate Weibo features and check whether they can help improve the system performance. In Table 3 and Table 4, we present the improvement ratios over baselines with the incorporation of Weibo features. We can see that Weibo features yield a large improvement over the baseline system, especially when the size of history window is small, i.e., 3 question threads. This indicates the effectiveness of Weibo features on alleviating the cold-start problem in Zhihu. When we have more history data, i.e., 10 question threads, the improvement becomes smaller. It is noteworthy that the single prestige feature (i.e., P+UniformG and P+HisG) achieves good performance. Especially, P+HisG obtains very competitive results compared with the baseline system. P+HisG naturally combines history data on Zhihu and prestige information on Weibo, which largely improves the standard prestige estimation method P+UniformG. As a comparison, the relevance feature is not that effective but still improves the overall performance a bit. These findings indicate that the incorporation of social media data can be a very promising way to improve the tasks of startup services. History Window Size Systems MRR NULL P+UniformG Rel question threads (B)aseline P+HisG B.+Weibo vs. B % % +5.94% 5 question threads (B)aseline P+HisG B.+Weibo vs. B % % +6.27% 10 question threads (B)aseline P+HisG B.+Weibo vs. B % % +5.07% Table 4: Best answer prediction performance with varying history window sizes. *, **, *** indicate the improvement is significant at the level of 0.1, 0.05 and 0.01 respectively. 663

11 Chirag Shah and Jefferey Pomerantz Evaluating and predicting answer quality in community qa. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages ACM. Young-In Song, Jing Liu, Tetsuya Sakai, Xin-Jing Wang, Guwen Feng, Yunbo Cao, Hisami Suzuki, and Chin-Yew Lin Microsoft research asia with redmond at the ntcir-8 community qa pilot task. In NTCIR-8. Juyup Sung, Jae-Gil Lee, and Uichin Lee Booming up the long tails: Discovering potentially contributive users in community-based question answering services. Rui Yan, Mirella Lapata, and Xiaoming Li Tweet recommendation with graph co-ranking. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pages Association for Computational Linguistics. Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun, and Zhong Chen Cqarank: ointly model topics and expertise in community question answering. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages ACM. Reza Zafarani and Huan Liu Connecting users across social media sites: a behavioral-modeling approach. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages ACM. ChengXiang Zhai Statistical language models for information retrieval. Synthesis Lectures on Human Language Technologies, pages Yongzheng Zhang and Marco Pennacchiotti. 2013a. Predicting purchase behaviors from social media. In Proceedings of the 22nd international conference on World Wide Web, pages International World Wide Web Conferences Steering Committee. Yongzheng Zhang and Marco Pennacchiotti. 2013b. Recommending branded products from social media. In Proceedings of the 7th ACM conference on Recommender systems, pages ACM. Jun Zhang, Mark S Ackerman, and Lada Adamic Expertise networks in online communities: structure and algorithms. In Proceedings of the 16th international conference on World Wide Web, pages ACM. Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, and Xing Xie Mining novelty-seeking trait across heterogeneous domains. In Proceedings of the 23rd international conference on World wide web, pages International World Wide Web Conferences Steering Committee. Guangyou Zhou, Siwei Lai, Kang Liu, and Jun Zhao Topic-sensitive probabilistic model for expert finding in question answer communities. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages ACM. 666

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