Lanjun Zhou

Mainstream media behavior analysis on Twitter: a case study on UK general election

With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UKs 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results.

Web Information Mining and Decision Support Platform for the Modern Service Industry

This demonstration presents an intelligent information platform MODEST. MODEST will provide enterprises with the services of retrieving news from websites, extracting commercial information, exploring customers’ opinions, and analyzing collaborative/competitive social networks. In this way, enterprises can improve the competitive abilities and facilitate potential collaboration activities. At the meanwhile, MODEST can also help governments to acquire information about one single company or the entire board timely, and make prompt strategies for better support. Currently, MODEST is applied to the pillar industries of Hong Kong, including innovative finance, modem logistics, information technology, etc.

Pests Hidden in Your Fans: An Effective Approach for Opinion Leader Discovery

With the development of Web 2.0, people would like to share opinions on the Web, which are very helpful for other users to make decisions. Especially, some users have more powerful influence to other members of a community, group, or society, and their advice, opinions, and views are more valuable. We call these people opinion leaders. The study of opinion leader discovery from the social media is meaningful because it could help users to understand influential user behavior, and trace vital information diffusion of an e-society, even on-line ecology. However, existing approaches focus on linkage-based methods without considering the pests who have relationship with the potential opinion leader but carrying opposite opinions. In an extreme case, an opinion leader might be mistakenly identified according to his richer relationships with the pests. In this paper, we start from explaining the definition of opinion leader, and take into consideration of the user profile and posts’ opinions instead of using structural information (linkage) only. As such, those pests carrying opposite opinions could be gotten rid of from the social network, which could further improve the effectiveness of discovering opinion leaders. To evaluate the performance of our approach, we made experiments based on the Tweets data, and the results showed that our proposed approach could achieve 8% improvement compared with the linkage-based approach.

An effective approach for topic-specific opinion summarization

Topic-specific opinion summarization (TOS) plays an important role in helping users digest online opinions, which targets to extract a summary of opinion expressions specified by a query, i.e. topic-specific opinionated information (TOI). A fundamental problem in TOS is how to effectively represent the TOI of an opinion so that salient opinions can be summarized to meet users preference. Existing approaches for TOS are either limited by the mismatch between topic-specific information and its corresponding opinionated information or lack of ability to measure opinionated information associated with different topics, which in turn affect the performance seriously. In this paper, we represent TOI by word pair and propose a weighting scheme to measure word pair. Then, we integrate word pair into a random walk model for opinionated sentence ranking and adopt MMR method for summarization. Experimental results showed that salient opinion expressions were effectively weighted and significant improvement achieved for TOS.

An Efficient Approach for Sentence-based Opinion Retrieval

Recently, there is a growing interest in sharing personal opinions on the Web, such as product reviews, economic analysis, political polls, etc. Therefore, opinion retrieval, which targets to retrieve documents expressing opinions or comments about the query, has become more and more popular. A typical method for opinion retrieval is document-based and each document is assigned a relevant score and an opinionated score, respectively. Then the documents are ranking based on a combination of the two scores. In this method, however, the document is split into bag-of-word, and the association between the opinion and its corresponding target is broken. In an extreme case, a relevant document full of irrelevant opinions will also be retrieved. In this paper, we propose a sentence-based approach since opinions are always expressed in one sentence where the association between an opinion and its corresponding target is maintained. We assign an individual score to each sentence rather than assign an overall score to the document directly. Moreover, we consider the effectiveness of different positions of sentences in documents to further capture the structural information. Compared with document-based approaches, experimental results on our own dataset show that our approach has achieved significant improvement.