Modelling Polarity of Articles and Identifying Influential Authors through Social Movements

Sunflower Movement is one of the most influential social movements in Taiwan over the past few decades. In order to protest the review process at the legislature, the protesters entered and occupied the building of the Legislative Yuan (the parliament) of Taiwan on March 18, 2014, without any sign in advance. This action shook the government and caught a high level of attention in Taiwan. People discussed the action from different viewpoints and showed their supporting and opposition on the movement in daily life as well as on social media sites. However, the information is in chaos since a large number of articles had been published during the movement. In order to realize the social discussions in a comprehensive way, major issues such as extracting notable threads and finding important authors among social sites need to be addressed. In this paper, we provide methodologies to quantify and predict the polarity of each article, we also present a consensus-based approach to identify influential authors on the social forum. From our results, our proposal is effective and efficient for identifying information and authors that are worthy of attention through the social movement.

[1]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[2]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[3]  Summer Harlow,et al.  Social media and social movements: Facebook and an online Guatemalan justice movement that moved offline , 2012, New Media Soc..

[4]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[5]  Michelle R. Guy,et al.  Twitter earthquake detection: earthquake monitoring in a social world , 2012 .

[6]  Eugene Agichtein,et al.  Discovering authorities in question answer communities by using link analysis , 2007, CIKM '07.

[7]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[8]  Fernando Diaz,et al.  Extracting information nuggets from disaster- Related messages in social media , 2013, ISCRAM.

[9]  M. Lim Clicks, Cabs, and Coffee Houses: Social Media and Oppositional Movements in Egypt, 2004–2011 , 2012 .

[10]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[11]  Bernard J. Jansen,et al.  Micro-blogging as online word of mouth branding , 2009, CHI Extended Abstracts.

[12]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[13]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[14]  E. Katz The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis , 1957 .

[15]  A. Bruns,et al.  #qldfloods and @QPSMedia: Crisis Communication on Twitter in the 2011 South East Queensland Floods , 2012 .

[16]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[17]  Junseok Hwang,et al.  Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach , 2012 .

[18]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[19]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[20]  Chin-Laung Lei,et al.  Forecasting the Impacts of Articles and Authors on the Social Forum during Emergencies , 2014, ICS.

[21]  Feng Li,et al.  Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs , 2011, Decis. Support Syst..