Xujuan Zhou

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.

Associations between exposure to and expression of negative opinions about Human Papillomavirus vaccines on social media: an observational study

Background Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities. Objective We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities. Methods We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample. Results During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001). Conclusions The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions.

Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the users search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval

A two-stage text mining model for information filtering

Mismatch and overload are the two fundamental issues regarding the effectiveness of information filtering. Both term-based and pattern (phrase) based approaches have been employed to address these issues. However, they all suffer from some limitations with regard to effectiveness. This paper proposes a novel solution that includes two stages: an initial topic filtering stage followed by a stage involving pattern taxonomy mining. The objective of the first stage is to address mismatch by quickly filtering out probable irrelevant documents. The threshold used in the first stage is motivated theoretically. The objective of the second stage is to address overload by apply pattern mining techniques to rationalize the data relevance of the reduced document set after the first stage. Substantial experiments on RCV1 show that the proposed solution achieves encouraging performance.

Sentiment analysis on tweets for social events

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of peoples opinions. Tweets data can be efficiently used to infer peoples opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general peoples opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.

Using Information Filtering in Web Data Mining Process

The amount of Web information is growing rapidly, improving the efficiency and accuracy of Web information retrieval is uphill battle. There are two fundamental issues regarding the effectiveness of Web information gathering: information mismatch and overload. To tackle these difficult issues, an integrated information filtering and sophisticated data processing model has been presented in this paper. In the first phase of the proposed scheme, an information filter that based on user search intents was incorporated in Web search process to quickly filter out irrelevant data. In the second data processing phase, a pattern taxonomy model (PTM) was carried out using the reduced data. PTM rationalizes the data relevance by applying data mining techniques that involves more rigorous computations. Several experiments have been conducted and the results show that more effective and efficient access Web information has been achieved using the new scheme.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter.

The manner in which people preferentially interact with others like themselves suggests that information about social connections may be useful in the surveillance of opinions for public health purposes. We examined if social connection information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers that identify anti-vaccine opinions. From 42,533 tweets posted between October 2013 and March 2014, 2,098 were sampled at random and two investigators independently identified anti-vaccine opinions. Machine learning methods were used to train classifiers using the first three months of data, including content (8,261 text fragments) and social connections (10,758 relationships). Connection-based classifiers performed similarly to content-based classifiers on the first three months of training data, and performed more consistently than content-based classifiers on test data from the subsequent three months. The most accurate classifier achieved an accuracy of 88.6% on the test data set, and used only social connection features. Information about how people are connected, rather than what they write, may be useful for improving public health surveillance methods on Twitter.

Towards Context-Sensitive Domain Ontology Extraction

Although there has been a surge of interest in applying domain ontologies to facilitate communications among computers and human users, engineering of these ontologies turns out to be very labor intensive and time consuming. Recently, some learning methods have been proposed for automatic or semi-automatic extraction of ontologies. Nevertheless, the accuracy and computational efficiency of these methods should be improved to support large scale ontology extraction for real-world applications. This paper illustrates a novel domain ontology extraction method. In particular, contextual information of the knowledge sources is exploited for the extraction of high quality domain ontologies. By combining lexico-syntactic and statistical learning approaches, the accuracy and the computational efficiency of the extraction process can be improved. Empirical studies have confirmed that the proposed method can extract reliable domain ontology to improve the performance of information retrieval and facilitate human users to discover and refine domain ontology

Financial competing interests were associated with favorable conclusions and greater author productivity in nonsystematic reviews of neuraminidase inhibitors

OBJECTIVE To characterize the conclusions and production of nonsystematic reviews about neuraminidase inhibitors relative to financial competing interests held by the authors. STUDY DESIGN AND SETTING We searched for articles about neuraminidase inhibitors and influenza (January 2005 to April 2015), identifying nonsystematic reviews and grading them according to the favorable/nonfavorable presentation of evidence on safety and efficacy. We recorded financial competing interests disclosed in the reviews and from other articles written by their authors. We measured associations between competing interests, author productivity, and conclusions. RESULTS Among 213 nonsystematic reviews, 138 (65%) presented favorable conclusions. Financial competing interests were identified for 26% (137/532) of authors; 51% (108/213) of reviews were associated with a financial competing interest. Reviews produced exclusively by authors with financial competing interests (33%; 71/213) were more likely to present favorable conclusions than reviews with no competing interests (risk ratio 1.27; 95% confidence interval 1.03-1.55). Authors with financial competing interests published more articles about neuraminidase inhibitors than their counterparts. CONCLUSION Half of nonsystematic reviews about neuraminidase inhibitors included an author with a financial competing interest. Reviews produced exclusively by these authors were more likely to present favorable conclusions, and authors with financial competing interests published a greater number of reviews.