A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally 'Sina microblogging'). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the 'audience' in their expertise domains.

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users’ influence scores. They rarely consider a person’s expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally ”Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of “top across-domain influencers”. In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on the “audience” in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.

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 UK's 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. Copyright 2013 ACM.

Governmental social media use for emergency communication

The possibility of crowdsourced information, multi-geographical and multi-organisational information flows during emergencies and crises provided by web 2.0 tools are providing emergency management centres with new communication challenges and opportunities. Building on the existing emergency management and social media literature, this article explores how institutions are using and adopting social media for emergency communication. By examining the drivers and barriers of social media adoption in two European governmental agencies dealing with emergencies, the paper aims to establish a framework to examine whether and how institutional resilience could be improved.

New perspectives on the governance of executive compensation:an examination of the role and effect of compensation consultants

We develop a multi-theoretic approach, drawing on economic, institutional, managerial power and social comparison literatures to explain the role of the external compensation consultant in the top management pay setting institutional field. Taking advantage of recent disclosure requirements in the UK, we collect data on compensation consultant use in 232 large companies. We show that consultants are a prevalent part of the CEO pay setting scene, and document evidence of all advisor use. Our econometric results show that consultant use is associated with firm size and the equity pay mix. We also show that CEO pay is positively associated with peer firms that share consultants, with higher board and consultant interlocks, and some evidence that where firms supply other business services to the firm, CEO pay is greater. © 2009 Springer Science+Business Media, LLC.

Exploring the enterprise value of wikis through media choice theories

Wikis are quickly emerging as a new corporate medium for communication and collaboration. They allow dispersed groups of collaborators to asynchronously engage in persistent conversations, the result of which is stored on a common server as a single, shared truth. To gauge the enterprise value of wikis, the authors draw on Media Choice Theories (MCTs) as an evaluation framework. MCTs reveal core capabilities of communication media and their fit with the communication task. Based on the evaluation, the authors argue that wikis are equivalent or superior to existing asynchronous communication media in key characteristics. Additionally argued is the notion that wiki technology challenges some of the held beliefs of existing media choice theories, as wikis introduce media characteristics not previously envisioned. The authors thus predict a promising future for wiki use in enterprises.