電視播報選舉新聞之政黨偏差研究--從視覺呈現和報導語言分析
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2010
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本研究目的在探討2008年總統選舉活動期間，有線電視報導重大選舉新聞事件時，是否產生政黨偏差，進而影響閱聽人對於電視新聞台也產生政黨偏差之觀感。
本研究以立意抽樣方式選取TVBS-N與SET-N兩家電視新聞台，於單日收視率最高之19：00~20：00時段，報導「312維新館事件」、「316藍綠大遊行」、「321選前之夜」等三大選舉議題，針對新聞報導數量、新聞報導次序編排、新聞報導語意向度等三大面向進行檢驗，電視新聞報導選舉議題時是否產生顯著之政黨偏差。其中，語意向度包括視覺呈現和報導語言，本研究以文本分析法深入探討。另外，在新聞報導數量與新聞報導次序編排部分，則採內容分析法進行檢驗。
研究結果顯示，在報導「312維新館事件」時，TVBS-N在新聞報導量上傾向淡化處理國民黨負面新聞，SET-N則反之。新聞編排方面，SET-N傾向集結對民進黨友善之訊息。語意向度方面，TVBS-N之新聞標題與視覺畫面均傾向對國民黨友善，而SET-N之新聞標題傾向對國民黨不友善，視覺畫面傾向對民進黨友善。在報導「316藍綠大遊行」事件時，SET-N傾向提供民進黨較多新聞則數與聲刺長度之報導數量，語意向度之視覺畫面亦較TVBS-N傾向對國民黨不友善；新聞旁白之干擾性聲調，SET-N與TVBS-N均以對民進黨友善之分配比例最高。在報導「321選前之夜」事件時，SET-N傾向提供民進黨之新聞報導數量較國民黨為多，TVBS-N則反之。次序編排方面，TVBS-N傾向以國民黨編排於前段，SET-N則反之。語意向度之視覺呈現方面，TVBS-N與SET-N均將國民黨與民進黨之造勢晚會分別以均等雙分割及子母畫面共同呈現。而語意向度之新聞標題，TVBS-N與SET-N均傾向對民進黨友善；新聞旁白之干擾性聲調，TVBS-N則傾向對國民黨友善，而SET-N則傾向中立。
本研究結果將提供電視新聞台在產製選舉新聞時，是否具有政黨偏差之檢驗模式，避免成為某特定政黨之傳聲筒，同時亦提供學界做為媒體識讀教材之參考，培養耳聰目明的閱聽人。
《Abstract》 This thesis aims to elaborate whether or not a partisan bias exists in the TV news for the 2008 Presidential Election , and whether it in terms also invokes similar feelings of partisanship in the audience. The audience rating from 7pm to 8pm everyday and used Content Analysis. The thesis analyses TV reports from two TV stations, TVBS-N and SET-N, about three events: “312 Wei-xin incident”, “316 Partisan parades”, and “321 The last campaign night”. These events were caused by either KMT or DPP, the two main political parties in Taiwan, and thus these items included conflicts from different political stance. The research methods are quantitative content analysis and contextual analysis. Based on primetime news during the election period, this paper focuses on three aspects of these reports, the quantity, the rundown, and the semantic turn. The semantic turn refers to the visual presentation and the language used, and the language material includes: news titles, the oral script, and the sound bites. Three results were derived from these methods. First, TVBS-N tends to downplay the negative coverage of the KMT. On the contrary, SET-N tends to exaggerate them. Second, SET-N tends to concentrate on broadcasting positive coverage of the DPP. Third, the headlines and videos used by TVBS-N tend to more friendly to the KMT. Those of SET-N tend to more unfriendly. The research also found: First, as regards the news coverage of the Wei-xin incident, TVBS-N tended to downplay the negative coverage of the KMT and the SET-N tended to exaggerate KMT's negative images. In terms of the semantic turn, the TVBS-N was more friendly to KMT, while SET-N was more unfriendly to KMT in titles and more friendly to DPP in visual presentation. Second, during the KMT and DPP parades, SET-N offered more coverage and sound-bites from the DPP, while the semantic turn was less friendly to the KMT than TVBS. In terms of OS, both TV stations showed friendly attitude toward the DPP. Third, as for the news coverage of the last campaign night, the headline and video footage of TVBS-N was perceived as more favorable toward KMT, while SET-N favored the DPP. In terms of separate items: both TV stations showed almost identical visual presentation, both stations offered news titles favorable to DPP, and the OS of TVBS-N was favorable to KMT while SET-N remained neutral. This research offers a model to examine partisan bias in TV news. Hopefully, the result could help prevent TV stations from being a tool of political propaganda, and also provides academic references for media literacy.
《Abstract》 This thesis aims to elaborate whether or not a partisan bias exists in the TV news for the 2008 Presidential Election , and whether it in terms also invokes similar feelings of partisanship in the audience. The audience rating from 7pm to 8pm everyday and used Content Analysis. The thesis analyses TV reports from two TV stations, TVBS-N and SET-N, about three events: “312 Wei-xin incident”, “316 Partisan parades”, and “321 The last campaign night”. These events were caused by either KMT or DPP, the two main political parties in Taiwan, and thus these items included conflicts from different political stance. The research methods are quantitative content analysis and contextual analysis. Based on primetime news during the election period, this paper focuses on three aspects of these reports, the quantity, the rundown, and the semantic turn. The semantic turn refers to the visual presentation and the language used, and the language material includes: news titles, the oral script, and the sound bites. Three results were derived from these methods. First, TVBS-N tends to downplay the negative coverage of the KMT. On the contrary, SET-N tends to exaggerate them. Second, SET-N tends to concentrate on broadcasting positive coverage of the DPP. Third, the headlines and videos used by TVBS-N tend to more friendly to the KMT. Those of SET-N tend to more unfriendly. The research also found: First, as regards the news coverage of the Wei-xin incident, TVBS-N tended to downplay the negative coverage of the KMT and the SET-N tended to exaggerate KMT's negative images. In terms of the semantic turn, the TVBS-N was more friendly to KMT, while SET-N was more unfriendly to KMT in titles and more friendly to DPP in visual presentation. Second, during the KMT and DPP parades, SET-N offered more coverage and sound-bites from the DPP, while the semantic turn was less friendly to the KMT than TVBS. In terms of OS, both TV stations showed friendly attitude toward the DPP. Third, as for the news coverage of the last campaign night, the headline and video footage of TVBS-N was perceived as more favorable toward KMT, while SET-N favored the DPP. In terms of separate items: both TV stations showed almost identical visual presentation, both stations offered news titles favorable to DPP, and the OS of TVBS-N was favorable to KMT while SET-N remained neutral. This research offers a model to examine partisan bias in TV news. Hopefully, the result could help prevent TV stations from being a tool of political propaganda, and also provides academic references for media literacy.
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Keywords
電視新聞, 政黨偏差, 語意向度, 媒體識讀, TV News, Partisan Bias, Semantic Turn, Media Literacy