Conference Paper

OOLAM: An opinion oriented link analysis model for influence persona discovery

DOI: 10.1145/1935826.1935915 Conference: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February 9-12, 2011
Source: DBLP


Social influence is a complex and subtle force that governs the dynamics of social networks. In the past years, a lot of research work has been conducted to understand the spread patterns of social influence. However, most of approaches assume that influence exists between users with active social interactions, but ignore the question of what kind of influence happens between them. As such one interesting and also fundamental question is raised here: "in a social network, could the social connection reflect users'influence from both positive and negative aspects?". To this end, an Opinion Oriented Link Analysis Model (OOLAM) is proposed in this paper to characterize users' influence personae in order to exhibit their distinguishing influence ability in the social network. In particular, three types of influence personae are generalized and the problem of influence persona discovery is formally defined. Within the OOLAM model, two factors, i.e., opinion consistency and opinion creditability, are defined to capture the persona information from public opinion perspective. Extensive experimental studies have been performed to demonstrate the effectiveness of the proposed approach on influence persona analysis using real web data sets.

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Available from: Li Zhang, Nov 12, 2015
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    • "Negative persona represents bloggers with high negative influence, and their links from others usually express disagreement or distrust. The Controversial persona represents bloggers that are both likely to be challenged or supported by many (Cai et al., 2011), which is shown in the high number of agreement and disagreement inlink blog posts. Blog influence is specific to the topics or aspects discussed in the blog posts (Agarwal, Liu, Tang, & Yu, 2008; Somasundaran & Wiebe, 2010). "
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    ABSTRACT: Blogs are readily available sources of opinions and sentiments which allows bloggers to exert a certain level of influence over the blog readers. Previous studies had attempted to analyze blog features to detect influence within the blogosphere, but had not studied in details influence at the blogger-level. Other studies studied bloggers' personalities with regards to their propensity to blog, but did not relate the personalities of bloggers to influence. Bloggers may differ in their way or manner of exerting influence. For example, bloggers could be active participants or just passive shares, or whether they express ideas in a rational or subjective manner, or they are received positively or negatively by the readers. In this paper, we further analyze the engagement style (frequency, scope, originality, and consistency of the blog postings), persuasion style (appeals to reasons or emotions), and persona (degree of compliance) of individual bloggers. Methods used include similarity analysis to detect the sharing-creating aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. While previous studies analyzed influence at blog site level, our model is shown to provide a fine-grained influence analysis that could further differentiate the bloggers' influence style in a blog site.
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    • "Leskovec et al. [7] used logistic regression to predict the signs of edges in signed networks by exploiting 7-dimensional degree features and 16-dimensional triad features. Cai et al. [3] proposed another feature (i.e., influence) aside from the 7-dimensional degree features in [7]. A PageRank-like algorithm was developed to compute the influence of individual users and then use it as another feature in an SVM classifier to predict the signs of edges. "
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    ABSTRACT: Social influence analysis in online social networks is the study of people's influence by analyzing the social interactions between individuals. There have been increasing research efforts to understand the influence propagation phenomenon due to its importance to information dissemination among others. Despite the progress achieved by state-of-the-art social influence analysis techniques, a key limitation of these techniques is that they only utilize positive interactions (e.g., agreement, trust) between individuals, ignoring two equally important factors, namely, negative relationships (e.g., distrust, disagreement) between individuals and conformity of people, which refers to a person's inclination to be influenced. In this paper, we propose a novel algorithm CASINO (Conformity-Aware Social INfluence cOmputation) to study the interplay between influence and conformity of each individual. Given a social network, CASINO first extracts a set of topic-based subgraphs where each subgraph depicts the social interactions associated with a specific topic. Then it optionally labels the edges (relationships) between individuals with positive or negative signs. Finally, it computes the influence and conformity indices of each individual in each signed topic-based subgraph. Our empirical study with several real-world social networks demonstrates superior effectiveness and accuracy of CASINO compared to state-of-the-art methods. Furthermore, we revealed several interesting characteristics of "influentials" and "conformers" in these networks.
    Full-text · Conference Paper · Jan 2011
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