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| Title: | 探索網路新聞主題與政黨傾向：語意網絡分析之環境公投研究應用
Explore Partisan Slant of Online News Framing: A Semantic Network Analysis on an Environmental Referendum Issue |
| Authors: | 高千容
Kao, Chien-Jung |
| Contributors: | 林翠絹
Lin, Tsui-Chuan
高千容
Kao, Chien-Jung |
| Keywords: | 語意網絡分析
政黨傾向
網路新聞
社群媒體
公投
環境
Partisan Slant
Semantic Network Analysis
Online News Media
Social Media
Referendum
Taiwan |
| Date: | 2020 |
| Issue Date: | 2020-04-06 14:45:55 (UTC+8) |
| Abstract: | In Taiwan, the project of “Shenao thermal power plant” expansion (Ministry of Economics Affairs, 2018) was opposed by environmental groups and local residents (Liao, 2018; Greenpeace, 2018; DeAeth, 2018). The divergent discussions on Shenao power plant issue was voted in 2018 Referendum in order to seek public opinions (Ferry, 2018b). As politics in Taiwan is characterized as a two-party system consisting of Kuo Ming Tang (KMT)- led political alliance and Democracy Progress Party-led (DPP) league (Hsiao, 2014), perspectives about environmental issues inevitably have been divided into two types of claims supported by different political parties and ideologies. The news reporting about Shenao power plant in both traditional and digital-native media showed struggles and conflicts between KMT and DPP (ETtoday News, 2018; Hsu, 2018; Chang, 2018).
Slant or bias in news involves selections of news media contents (Schudson, 2003). Some mainstream media showed partisan slant which were in favor of a political party (Merkley, 2019). Past studies found it difficult to operationalize concepts of “bias” or “balance” (Hopmann, Van Aelst & Legnante, 2012; Lichter, 2017). Researchers focused on content-based analysis, considering that framing or text-based factors as a crucial factor of slant. In order to cover large amount of news contents, these studies only measure partisan slant based on frequency of mentions. Alternatively, this research proposed to use semantic network analysis to examine the selected issues regarding partisan slant in news media framing.
Before exploring the partisan slant, this research investigated news media’s framing in environmental news coverage during 2018 referendum. This study’s RQ1 examined the salient issues and framing of the Shenao power plant issues in different media types (traditional news media, digital-native news media, and social media). It further compared salient issues of Facebook posts from fan pages of KMT/DPP political actors (politicians or political organizations) and environmental group fan pages related to the Shenao power plant issue. RQ2 explored the partisan slant of news media on Shenao power plant referendum issue, which regarded social media contents from two political parties (KMT and DPP) as the parameters to measure news media’s partisan slant. The environmental groups were used as the non-partisan parameter for comparison. RQ2a measured the partisan slant of two types of news media, while RQ2b identified partisan slant of all news media. The semantic network correlation analysis utilized Quadratic Assignment Procedure (QAP) (Krackhardt, 1988) to identify the partisan leaning of news media. Finally, RQ3 investigated the salient issues in news media which were labelled their partisan leaning as pro-KMT or pro-DPP categories.
This research conducted analysis with two datasets which included uMiner’s online media news articles and QSearch’s Facebook posts dated July 1 to November 30, 2018. This research considered the Environmental Protect Administration’s (EPA) official announcement on June 28 that Shenao power plant expansion project was approved by environmental impact assessment review as the the start for data collection, and it ended at six days after the referendum on 24 November 2018. Through Boolean keywords, uMiner platform provided news contents regarding Shenao power plant issue, consisting of 2,627 articles, including 1,597 articles from six traditional media brands (61%) and 1,030 articles from four digital native news media (39%). QSearch Trend panel provided 817 Facebook posts related to Shenao power plant (550 from KMT, 45 posts from DPP, and 222 from environmental group fan pages). As for data analysis, RQ1 used the two dataset, while RQ2, and RQ3 used the same news media dataset but different Facebook dataset. In order eliminate biases, RQ2 and RQ3 utilized the different Facebook dataset through random sampling on Facebook posts composed of 135 posts, including 45 posts from DPP, KMT and environmental group pages, which would keep the same article number as the three parameters to measure slant.
The results showed that the Shenao Power Plant-related topics mentioned by all communicators (i.e., DPP/KMT political actors and environmental groups on Facebook, traditional news media and digital native news media) focused on three dimensions: Shenao power plant, politics and Government, and environmental concerns. The terms of two political parties (KMT and DPP) dominated the top-ten salient issues among all the communicators except for the environmental groups. The environmental groups on Facebook focused more on the environmental concerns in their top-ten salient issues. As for the correlation analysis, both traditional media and digital native media were significantly correlated to a greater degree to the environment network, rather than correlated to DPP or KMT actors. That is, what the news media reported on Shenao power plant issue was closely similar to what environmental groups discussed about, rather than political actors’ discussion focus. Both traditional news media and digital native media showed more partisan slant towards KMT actors. As for the partisan slant of news media brands, eight media leaned towards KMT actors (pro-KMT), but one digital native media and one traditional news media showed more correlated to DPP actors (pro-DPP). Regarding the result of RQ3, the pro-DPP and pro-KMT media shared similar crucial concepts: Shenao power plant, two major political parties (DPP and KMT), and the words related to the government. That is, traditional and digital native news media focused on Shenao power plant issue, partisan, politics, and environmental concerns, indicating that despite partisan leaning, both media types mainly focused on similar themes in two political parties.
As for contributions, this study made efforts to fill the research gaps by investigating media slant, framing, semantic network analysis, Taiwan’s environmental concerns through semantic network analysis. As the measurements of media slant were still diverged (D`Alessio & Allen, 2000; Lichter, 2017), this research proposed to use the social media contents as parameters to measure the slant, extending the concepts of “partisan slant” or “media slant”. Additionally, this research extended the understanding of intermedia influences across media platforms through semantic way, providing more possibilities of using intermedia contents to measure partisan slant. Empricially, it explored the different framing and salient issues between news media social media, providing text-based research insights on Taiwan news research on referendum issues. With a two-system party politics, Taiwan encountered conflicts between the two major political parties (i.e., KMT and DPP), and based on the analysis of the Shenao plant case, the environmental issues turned into political framing in some reports on local news media. |
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| Description: | 碩士
國立政治大學
國際傳播英語碩士學位學程(IMICS)
106461009 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106461009 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000381 |
| Appears in Collections: | [國際傳播英語碩士學程] 學位論文
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