DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390139
Yi Zeng, Hongwei Hao, N. Zhong, X. Ren, Yan Wang
{"title":"Ranking and combining social network data for web personalization","authors":"Yi Zeng, Hongwei Hao, N. Zhong, X. Ren, Yan Wang","doi":"10.1145/2390131.2390139","DOIUrl":"https://doi.org/10.1145/2390131.2390139","url":null,"abstract":"Various Web-based social network data reflect user interests from multiple perspectives in a distributed environment. They need to be integrated for better user modelling and personalized services. We argue that in different scenarios, different social networks play different roles and their degrees of importance are not equivalent. Hence, ranking strategies among different social network data sources are needed. In addition, combining different social network data can produce interesting subsets of these data with different levels of importance. In this paper, we propose social network data ranking and composition strategies, we validate the proposed methods by collaboration network data (Semantic Web Dog Food) and micro-blogging data (from Twitter), then we use the ranked and composed results for developing a Web-based personalized academic visit recommendation system to show their potential effectiveness.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390146
P. Chundi, April L. Corbet
{"title":"Analyzing sentiments from street harassment stories","authors":"P. Chundi, April L. Corbet","doi":"10.1145/2390131.2390146","DOIUrl":"https://doi.org/10.1145/2390131.2390146","url":null,"abstract":"Street harassment is a pervasive problem that typically targets women and LGBTQ community. There are no effective ways to deal with the harassers as the acts of harassment happen randomly and are difficult, if not impossible, to prosecute. Hollaback! is an international movement aimed at stopping street harassment. Hollaback! servers collect street harassment stories from victims around the globe to share, gather statistics, and create awareness. In this paper, we present a preliminary study focused on analyzing a small sample of Hollaback! stories submitted from New York city. The LIWC software [1] is used to measure the positive and negative emotions hidden in each story and correlate it to the socio-economic status of the location from which the story was submitted.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390136
Ana-Maria Popescu
{"title":"Pinteresting: towards a better understanding of user interests","authors":"Ana-Maria Popescu","doi":"10.1145/2390131.2390136","DOIUrl":"https://doi.org/10.1145/2390131.2390136","url":null,"abstract":"The ascendance of interest-based social networks allows for developing better models of user interest in the context of established and new tasks, connecting interests with demographic characteristics, studying changes in user interests with respect to time and location or mining interest data to build broadly useful resources. This submission focuses on Pinterest, a growing social bookmarking site; it identifies research avenues for improving the user experience and for exploiting Pinterest data for broader uses.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123715105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390134
Victor C. Liang, V. Ng
{"title":"A collective synchronous behavior model on social media","authors":"Victor C. Liang, V. Ng","doi":"10.1145/2390131.2390134","DOIUrl":"https://doi.org/10.1145/2390131.2390134","url":null,"abstract":"Collective synchronous behavior is a pervasive phenomenon that we can discover in nature and virtual social media. Traditional data mining methods, however, mainly concentrate on analysis of individual behavior. In sociology, many well-known models are not suitable for the social media environment as well, in which huge amounts of data are generated everyday. In this paper, we proposed an innovative model that consists of multiple hidden Markov chains. By learning from the observations from a group of people, our model can not only predict the steady future state of a collective, but also measure the dependency property, reactive factor, of individuals. Experiment result shows that our model has ability to distinguish the behaviors of different persons.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"257 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390145
Aditya Mogadala, Vasudeva Varma
{"title":"Twitter user behavior understanding with mood transition prediction","authors":"Aditya Mogadala, Vasudeva Varma","doi":"10.1145/2390131.2390145","DOIUrl":"https://doi.org/10.1145/2390131.2390145","url":null,"abstract":"Human moods continuously change over time. Tracking moods can provide important information about psychological and health behavior of an individual. Also, history of mood information can be used to predict the future moods of individuals. In this paper, we try to predict the mood transition of a Twitter user by regression analysis on the tweets posted over twitter time line. Initially, user tweets are automatically labeled with mood labels from time 0 to t-1. It is then used to predict user mood transition information at time t. Experiments show that SVM regression attained less root-mean-square error compared to other regression approaches for mood transition prediction.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121624191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390133
Jonghyun Han, Hyunju Lee
{"title":"Analyzing social media friendship for personalization","authors":"Jonghyun Han, Hyunju Lee","doi":"10.1145/2390131.2390133","DOIUrl":"https://doi.org/10.1145/2390131.2390133","url":null,"abstract":"Since social media users have various purposes such as tightening friendship and obtaining information, it might be easier to model a user's interest and to provide personalized information if the user's purpose can be inferred. In this paper, we analyze the friendship of Twitter users and its effects on Twitter usage. According to our analysis, although the number of offline friends is smaller than that of online friends, a user more actively responds to the microblogs posted by the offline friends. We expect that our analysis is helpful to model a user's social behavior and interest.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115222448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390140
Sheng Wang, Xiaobo Zhou, Ziqi Wang, Ming Zhang
{"title":"Please spread: recommending tweets for retweeting with implicit feedback","authors":"Sheng Wang, Xiaobo Zhou, Ziqi Wang, Ming Zhang","doi":"10.1145/2390131.2390140","DOIUrl":"https://doi.org/10.1145/2390131.2390140","url":null,"abstract":"Retweeting is the key mechanism of information diffusion on microblogging community. It is very challenging for user to choose the suitable tweets for retweeting, given the diverse and massive messages received and limited time on site. Therefore, it is crucial to design a recommender system that automatically recommends tweets for user to retweet. Recommending tweets for retweeting is different from conventional recommender system due to limited explicit feedback, high proportion of cold-start tweets and short tweet active time. In this paper, we propose a novel retweet recommendation (RTR) framework which leverages the implicit feedback to help user find the potential tweets he may want to retweet. RTR is divided into offline learning and online recommendation so that tweets can be taken into account as soon as it is published. In offline learning, we adapt a matrix factorization method based on BPR-OPT framework with implicit feedback to compensate the limited explicit feedback. RTR is able to recommend cold-start tweet based on its content. Extensive experiments on real-world microblogging community clearly show that RTR outperforms upon existing methods.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133936175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390137
Kosuke Takano, K. F. Li
{"title":"The framework of a people recommender based on a time series of user preferences","authors":"Kosuke Takano, K. F. Li","doi":"10.1145/2390131.2390137","DOIUrl":"https://doi.org/10.1145/2390131.2390137","url":null,"abstract":"In social media, it is important to build a community that includes people who share similar interests and purposes to foster further interaction and communication. In this study, we present a user profile construction method based on a time series of user preferences to allow the recommendation of appropriate people for the community. This method extracts user preferences as time series data by capturing the user's information browsing behavior in three information spaces: (1) a Web document information space, (2) an augmented reality information space, and (3) an interaction information space with applications for mobile devices. Our proposed method suggests potential members of a clique thus providing opportunities for users of social media to notice the implicit interests associated with other users based on their browsing behavior from the past to the current state, as well as to encourage social communication and relationships.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131426229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390143
Jacob Bollinger, D. Hardtke, Ben Martin
{"title":"Using social data for resume job matching","authors":"Jacob Bollinger, D. Hardtke, Ben Martin","doi":"10.1145/2390131.2390143","DOIUrl":"https://doi.org/10.1145/2390131.2390143","url":null,"abstract":"Bright has built an automated system for ranking job candidates against job descriptions. The candidate's resume and social media profiles are interwoven to build an augmented user profile. Similarly, the job description is augmented by external databases and user-generated content to build an enhanced job profile. These augmented user and job profiles are then analyzed in order to develop numerical overlap features each with strong discriminating power, and in sum with maximal coverage. The resulting feature scores are then combined into a single Bright Score using a custom algorithm, where the feature weights are derived from a nation-wide and controlled study in which we collected a large sample of human judgments on real resume-job pairings. We demonstrate that the addition of social media profile data and external data improves the classification accuracy dramatically in terms of identifying the most qualified candidates.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DUBMMSM '12Pub Date : 2012-10-29DOI: 10.1145/2390131.2390147
David García, F. Schweitzer
{"title":"Modeling online collective emotions","authors":"David García, F. Schweitzer","doi":"10.1145/2390131.2390147","DOIUrl":"https://doi.org/10.1145/2390131.2390147","url":null,"abstract":"A common phenomenon on the Internet is the appearance of collective emotions, in which many users share an emotional state. Online communities allow users to emotionally interact with large amounts of other users, creating collective states faster than in offline interaction. We present our modeling framework for collective emotions in online communities. This framework allows the analysis and design of agent-based models, including the dynamics of psychological states under emotional interaction. We illustrate the applications of our framework through an overview of two different models. Based on this framework, our first model of emotions in product reviews communities reproduces the empirical distribution of emotions towards products in Amazon. The second model within our framework reproduces the emergence of emotional persistence at the individual and collective level. This persistence pattern is similar to the one revealed by our statistical analysis of IRC chatrooms. Further applications of our framework aim at reproducing collective features of emotions in a variety of online communities.","PeriodicalId":352894,"journal":{"name":"DUBMMSM '12","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134358421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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