Automatic Content Analysis of Media Framing by Text Mining Techniques

Political-related news is one of the most popular topics in various media platforms. When news is produced through a process of selection and rephrase by reporters and media firms, reporters' personal political leaning and personal opinions may influence the process, important messages may inevitably loss. In Taiwan, the Parliamentary Library of Legislative Yuan website provides detailed contents about activities happening in the Legislative Yuan, including such contents as transcripts and video recordings of interpellation, conference speech, interim and legislation proposals. Although there is a complete record of information provided online, but the quantity of the legislative documents are far too much for citizens to make sense of. It is imperative that better organized information released to the public would facilitate readers to reduce the cognitive loads in understanding what issues have been discussed by legislators and reported by the media. To minimize the gap between legislative documents and the general public, this study proposes a text mining mechanism to automatically cluster legislative and news documents to identify media frames, and then represents the proportion of each frame corresponding to information sources. The automatic clustering system can determine media frames with the minimum amount of human interference. The results of interviews show that the information system proposed in this study is able to provide political domain experts hard evidences of media framing, and assist the public to discover media framing phenomenon, which are the major contributions of this research.

[1]  Jian Hu,et al.  Mining multilingual topics from wikipedia , 2009, WWW '09.

[2]  Fu-Ren Lin,et al.  Automatic Content Analysis of Legislative Documents by Text Mining Techniques , 2015, 2015 48th Hawaii International Conference on System Sciences.

[3]  Adam D. Carton,et al.  Media framing of proposed nuclear reactors: An analysis of print media , 2010 .

[4]  L. Infante,et al.  Hierarchical Clustering , 2020, International Encyclopedia of Statistical Science.

[5]  James W. Dearing,et al.  Framing Public Life: Perspectives on Media and Our Understanding of the Social World , 2002 .

[6]  Brian G. Knight,et al.  Media Bias and Influence: Evidence from Newspaper Endorsements , 2008 .

[7]  Tim Groseclose,et al.  A Measure of Media Bias , 2005 .

[8]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[9]  Stefan Kaufmann,et al.  Classifying Party Affiliation from Political Speech , 2008 .

[10]  Robert M. Entman,et al.  Framing: Toward Clarification of a Fractured Paradigm , 1993 .

[11]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[12]  Bonnie Kaplan,et al.  Qualitative Research Methods for Evaluating Computer Information Systems , 2005 .

[13]  D. J. Van der Pas.  Making Hay While the Sun Shines , 2014 .

[14]  R. Krishnamoorthy,et al.  New optimized agglomerative clustering algorithm using multilevel threshold for finding optimum number of clusters on large data set , 2012, 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET).

[15]  Joan Morris DiMicco,et al.  Many bills: engaging citizens through visualizations of congressional legislation , 2011, CHI.

[16]  Bias in the news , 1976 .

[17]  R. Entman.  Framing Bias: Media in the Distribution of Power , 2007 .

[18]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[19]  Sean Gerrish,et al.  Predicting Legislative Roll Calls from Text , 2011, ICML.

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Salvatore T. March,et al.  Design and natural science research on information technology , 1995, Decis. Support Syst..

[22]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[23]  D. White.  The “Gate Keeper”: A Case Study in the Selection of News , 1950 .

[24]  Robert M. Entman,et al.  Media framing biases and political power: Explaining slant in news of Campaign 2008 , 2010 .

[25]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[26]  Gregor Heinrich.  Parameter estimation for text analysis , 2009 .

[27]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[28]  Hsin-Hsi Chen,et al.  An NLP & IR approach to topic detection , 2002 .

[29]  Peter J. Denning,et al.  A new social contract for research , 1997, CACM.

[30]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[31]  N. Huijboom,et al.  Open data: An International comparison of strategies , 2011 .

[32]  Jr. James W.Tankard.  The Empirical Approach to the Study of Media Framing , 2001 .

[33]  Takeo Kanade,et al.  Maximum Entropy for Collaborative Filtering , 2004, UAI.