The electronic cigarette (e-cigarette) market has grown rapidly in recent years. However, causes of e-cigarette related symptoms among users and their impact on health remain uncertain. This research ...aims to mine the potential relationships between symptoms and e-liquid components, such as propylene glycol (PG), vegetable glycerine (VG), flavor extracts, and nicotine, using user-generated data collected from Reddit.
A total of 3605 e-liquid related posts from January 1st, 2011 to June 30th, 2015 were collected from Reddit. Then the patterns of VG/PG distribution among different flavors were analyzed. Next, the relationship between throat hit, which was a typical symptom of e-cigarette use, and e-liquid components was studied. Finally, other symptoms were examined based on e-liquid components and user sentiment.
We discovered 3 main sets of findings: 1) We identified three groups of flavors in terms of VG/PG ratios. Fruits, cream, and nuts flavors were similar. Sweet, menthol, and seasonings flavors were classified into one group. Tobacco and beverages flavors were the third group. 2) Throat hit was analyzed and we found that menthol and tobacco flavors, as well as high ratios of PG and nicotine level, could produce more throat hit. 3) A total of 9 systems of 25 symptoms were identified and analyzed. Components including VG/PG ratio, flavor, and nicotine could be possible reasons for these symptoms.
E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and by policy makers to develop new regulations.
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We propose a design research approach combining behaviour and engineering techniques to better support user modeling in personalized mobile advertising applications. User modeling is a practical ...means of enabling personalization; one important method to construct user models is that of Bayesian networks. To identify the Bayesian network structure variables and the prior probabilities, empirical experimentation is adopted and context, content, and user preferences are the salient variables. User data collected from the survey are used to set the prior probabilities for the Bayesian network. Experimental evaluation of the system shows it is effective in improving user attitude toward mobile advertising.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the ...knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.
Online social network services (SNS) have been experiencing rapid growth in recent years. SNS enable users to identify other users with common interests, exchange their opinions, and establish forums ...for communication, and so on. Discovering densely connected user communities from social networks has become one of the major challenges, to help understand the structural properties of SNS and improve user-oriented services such as identification of influential users and automated recommendations. Previous work on community discovery has treated user friendship networks and user-generated contents separately. We hypothesize that these two types of information can be fruitfully integrated and propose a unified framework for user community discovery in online social networks. This framework combines the author-topic (AT) model with user friendship network analysis. We empirically show that this approach is capable of discovering interesting user communities using two real-world datasets.
► This paper presents a unified framework for user community detection in social network services. ► The framework integrates the user friendship networks and user-generated contents. ► It can help in understanding the structural properties of online social network services. ► Empirical evaluations on real-world datasets show the efficacy of the method.
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Micro-blogging is becoming an increasingly popular social media platform where users can discover interesting information about the real world and especially corporations are able to understand ...customers' demands. The fast diffusion of information and the convenience of micro-blogging have resulted in large audiences sharing their daily activities, exchanging opinions and establishing friendships with others. By analyzing the user-generated contents, one can explore users' potential interests, which helps micro-blogging provide users with better personalized information services. Users' behaviors are affected by opinions of their friends and changes in their interests over time. Based on these intuitions, in this paper we propose a temporal and social probabilistic matrix factorization model to predict users' potential interests in micro-blogging. By exploiting the matrix factorization technique to learn latent features of users and topics, our model analyzes the impacts of time information and users' activities, including posting of tweets and establishing friendships with others, on the latent feature space of users and topics of their interests. The proposed model provides a unified way to fuse the time information and the social network structure to predict users' future interests accurately. The experimental results on Sina-weibo, one of the most popular micro-blogging sites in China, demonstrate the efficiency and effectiveness of our proposed model.
► A new temporal and social PMF-based method is proposed to predict users' interests in micro-blogging. ► The model provides a unified way to fuse the time information and the social network structure. ► The experimental results demonstrate the efficiency and effectiveness of the model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Over the last few years, the social media site Flickr has gained massive popularity. Besides traditional operations on photo sharing, Flickr also offers millions of groups for users to join in order ...to share photos with others, and the number of groups still increases daily. Choosing among so many options is challenging for users. As such, helping users easily find their desirable groups has become increasingly important. In this paper, we provide a systematic experimental evaluation of several collaborative filtering algorithms to recommend groups for Flickr users. In particular, we design and compare seven Flickr group recommendation models: three memory-based models and four model-based models. Our results suggest that model-based approaches are beneficial compared with memory-based approaches in terms of top-
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recommendation metric. Models with tags perform well for sparse data, whereas models without tags are more suitable for dense data. Furthermore, incorporating tags in the recommendation algorithms leads to an improvement of precision on the top 2% performance.
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► This paper investigates the importance and usefulness of tag and time information when predicting users’ preference and how to exploit such information to build an effective resource-recommendation ...model in social tagging systems. ► A recommender system is built to realize the computational approach. ► Empirical results by using a real-world dataset show that tag and time information can well express users’ taste and better performances can be achieved if such information is integrated into collaborative filtering.
Recently, social tagging has become increasingly prevalent on the Internet, which provides an effective way for users to organize, manage, share and search for various kinds of resources. These tagging systems offer lots of useful information, such as tag, an expression of user’s preference towards a certain resource; time, a denotation of user’s interests drift. As information explosion, it is necessary to recommend resources that a user might like. Since collaborative filtering (CF) is aimed to provide personalized services, how to integrate tag and time information in CF to provide better personalized recommendations for social tagging systems becomes a challenging task.
In this paper, we investigate the importance and usefulness of tag and time information when predicting users’ preference and examine how to exploit such information to build an effective resource-recommendation model. We design a recommender system to realize our computational approach. Also, we show empirically using data from a real-world dataset that tag and time information can well express users’ taste and we also show that better performances can be achieved if such information is integrated into CF.
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Prestigious members on social networking websites are attracting increasing attentions from peers and corporations. People are used to consulting prestigious members for useful information and ...corporations are seeking opportunities to leverage them for “word of mouth” advertising. Identification and recognition of these prestigious members have been a crucial issue. Besides, the dynamic nature of members' behaviour determines the evolving nature of members' prestige. With the evolution of members' behaviour, currently prestigious members may be substituted by others who are not prestigious at present. The prediction of members' prestige evolution will help discover potential prestigious members, which will then help both people and corporations move to secure their long-term interests. However, little work has been done in relation to prediction of prestigious members. This paper aims to fill this gap specially using Flickr groups as a testbed. By investigating social actions among members, a graph-based action network framework to predict evolution of prestigious members has been proposed. Based on the social structural theory, which points out the interactive effect between social structure and users' actions, a favor action network that captures the social actions of choosing favourite photos of members is constructed. According to the network theory, properties of the nodes in the favor action network are investigated to identify currently prestigious members, and structural properties underlying the favor action network are mined to analyze the communication behaviour of members in a group. Further, the sociological theory inspires four key factors that affect members' favor action intentions, which are homophily, triadic interaction rule, continuity and recency. Based on the above analysis, a hybrid algorithm taking all these four factors into account is proposed to predict members' prestige evolution. Finally, several comprehensive and systematic analyses are designed and conducted to evaluate each of the functional components of the proposed framework. Results of evaluation on a real-world dataset validate its performance.
► This paper predicts evolution of prestigious members in Flickr groups. ► A graph-based framework is proposed to capture interactions among members. ► A hybrid algorithm taking four factors into account is proposed. ► Several comprehensive and systematic analyses are designed and conducted. ► Results of evaluation on a real-world dataset validate its performance.
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With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing ...answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision‐making basis for business.
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► We present a novel mechanism which can help users capture hot topics and their evolution trends in community question answering (CQA). ► A hot topic discovery and trend analysis system is developed ...for CQA. ► Empirical analyses using datasets from Yahoo! Answers show the efficacy of the system.
Community question answering (CQA) has recently become a popular social media where users can post questions on any topic of interest and get answers from enthusiasts. The variation of topics in questions and answers indicate the change of users’ interests over time. It can help users focus on the most popular products or events and track their changes by exploiting hot topics and analyzing the trend of a specific topic. In this paper, we present a hot topic detection and trend analysis system to capture hot topics in a CQA system and track their evolutions over time. Our system consists of hot term extraction, question clustering and trend analysis. Experimental results using datasets from Yahoo! Answers show that our system can discover meaningful hot topics. We also show that the evolution of topics over time can be accurately exploited by trend graphing.
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