Semantic networks and competition: Election year winners and losers in U.S. televised presidential debates, 1960–2004

Abstract

Drawing on network theory, this study considers the content of U.S. presidential debates and how candidates' language differentiates them. Semantic network analyses of all U.S. presidential debates (1960-2004) were conducted. Results reveal that regardless of party affiliation, election winners were more central in their semantic networks than losers. Although the study does not argue causation between debating and electoral outcomes, results show a consistent pattern: Candidates who develop coherent, central, semantically structured messages in debates seem to be victorious on election day. An argument is made for employing semantic networks in studying debates and political discourse.

Analyzing and modeling real-world phenomena with complex networks: a survey of applications

“…In another work, the US presidential debate transcripts from 1960 to 2004 originated a set of semantic networks that were analyzed through eigenvector centrality [493]. In that case, semantic networks were obtained from further processing of word co-occurrence data.…”

Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries

“…Finally, texts or documents can themselves be used as nodes and tied to the social agents (Hummon and Doreian, 1989;Moody, 2004). Of these three types, the present study focuses on semantic network analysis, primarily due to its capacity to extract salient information from text, to describe relationships among concepts, to analyze underlying meanings in text, and to understand the structure of concept networks (Danowski, 1982(Danowski, , 1993Carley and Palmquist, 1991;Rice & Danowski, 1993;Carley, 1997aCarley, , 1997bJang and Barnett, 1994;Doerfel, 1998;Popping, 2000Popping, , 2003Doerfel and Marsh, 2003;Diesner and Carley, 2004;Doerfel and Connaughton, 2009;Diesner, 2012).…”

“…First, concept networks, often called semantic networks, can be extracted (Sowa, 1984;Rice &Danowski, 1993;Jang and Barnett, 1994;Carley, 1997aCarley, , 1997bDoerfel and Barnett, 1999;Popping, 2000Popping, , 2003Smith, 2003;Smith and Humphreys, 2006;Doerfel & Marsh, 2003;Doerfel and Connaughton, 2009;Kwon et al, 2009;Carley et al, 2011). In these networks, nodes (i.e., concepts) represent salient information from a body of text and concepts (i.e., words) are "abstract representation of the information that people conceive in their minds" (Diesner, 2012: 5).…”

“…Since the early 1990s, network analysis of text data has become a widely used method in various academic fields including sociology (Carley, 1993), political science (Maynard, 1997;Kim, Su, & Hong, 2011), information science (Popping, 2003;Diesner and Carley, 2004;Doerfel and Connaughton, 2009), computer science (Diesner, 2012), communications (Rice and Danowski, 1993;Jang and Barnett, 1994;Doerfel, 1998;Doerfel and Barnett, 1999;Kwon et al, 2009;Doerfel and Marsh, 2003;van Atteveldt et al, 2008), conflict management (Young, 1996), poetics (Carley, 1994), and linguistics (Smith, 2003;Smith and Humphreys, 2006). As a result, many different theories and methods have been developed for text-based network analysis and there is no single agreed method and designation of the analysis in the literature (Doerfel, 1998;Diesner, 2012).…”

“…While traditional text analysis (i.e., content analysis) relies on measuring frequencies in order to find the most prominent concepts (Krippendorff, 2004), semantic network analysis can identify structural properties through recognition of relations between concepts (i.e., co-occurrence) by applying network analysis techniques. Through utilization of semantic network analysis, this study explores diverse structural properties such as bridging between concepts and sub-structures of the text, the interrelations of terms, and the most frequently mentioned concepts (Jang and Barnett, 1994;Carley, 1997aCarley, , 1997bDiesner andCarley, 2004, 2005;Kwon, 2009;Doerfel and Connaughton, 2009). …”