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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝吉隆(Ji-Lung Hsieh) | |
| dc.contributor.author | Chia-Yu Hsu | en |
| dc.contributor.author | 許家瑜 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:29:44Z | - |
| dc.date.available | 2021-03-05 | |
| dc.date.copyright | 2020-03-05 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-03-02 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64094 | - |
| dc.description.abstract | 新聞不僅提供資訊，同時也形塑公民意識和價值觀，其中新聞標題更負擔重要角色，影響閱聽人對報導的第一印象。然而現今線上新聞卻充斥誇張、未準確傳遞新聞資訊的標題，目的是希望誘發點擊超連結進入頁面以賺取廣告費，這樣的標題稱為誘餌式標題。為了解媒體如何再現新聞消息，本文針對新聞標題進行分析，以誘餌式標題角度切入，探究臺灣媒體在政治新聞標題上如何使用誘餌手法，包含使用了哪些特徵、與紙本新聞相比存在何種差異、同時也檢驗不同商業模式是否造成媒體在使用誘餌手法上有所不同。
本文立意蒐集2018年九合一選舉前三個月《中國時報》、《自由時報》、《ETToday新聞雲》、《中央社》線上政治新聞及《中國時報》、《自由時報》紙本新聞，透過情緒分析、詞性標記、熵等方法萃取了一百九十八個特徵，續以機器學習分類誘餌式標題，最終找出三十個重要特徵，分類有效評估F值達0.77。接下來，本研究比較了不同載體與不同經營模式媒體在這三十個特徵上的差異，研究結果發現，線上新聞比起紙本新聞確實可偵測到較多的誘餌標題，但情緒激起手法及使用不確定性用詞，也常見於紙本新聞。而比較不同經營模式的媒體則可以發現，非商業媒體幾乎不使用誘餌手法，即使使用，語境上也無影響新聞資訊準確性。本研究描繪了臺灣政治新聞使用誘餌式標題的使用策略，並針對中文書寫之特點，歸納出在地化的誘餌特性，可供後續研究使用。 | zh_TW |
| dc.description.abstract | News is not only part of communication that keeps us informed of the changing world, it frames the audience at the same time. Being the first impression of news, headlines play a crucial role by affecting the way the audience perceives the news content. However, floods of exaggerated headlines, information-omitted headlines or other so-called clickbait have threatened to drown online news in Taiwan. In order to know how media reproduce the news, this thesis focuses on clickbait to analysis what features do online news uses and what difference between cross-media.
This thesis uses online political news of China Times, Liberty Times, ETToday, Central News Agency, and printed version of China Times, Liberty Times to discuss the research question. We first consider a total of 198 features, filter out many features to avoid overfitting, and eventually select the 30 most important features. This article trains a machine learning model using logistic regression classifier to classify the clickbait-or-not of a given headline, and this method achieves an F1-score of 77%. The results indicate that online news tends to use clickbait indeed, but slightly different for some particular features. As for the dissimilar business model, non-commercial news agency barely uses clickbait and even if they do, it does not question the accuracy on the context. Since limited studies were conducted, this study is an exploratory analysis to describe the use of clickbait nowadays in Taiwan. Further, comparisons between media will offer some suggestions for following related researches. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:29:44Z (GMT). No. of bitstreams: 1 ntu-109-R06342003-1.pdf: 3245955 bytes, checksum: 8f36dbcc579905715247f54ed2ecd0d6 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 壹、緒論….………………………….………………………………… 1
一、研究背景……………………………………………………….. 1 二、研究目的與研究問題……………….…………………………. 4 貳、文獻回顧…….….…………………………………………… 7 一、誘餌式標題……………………………..……………………… 7 二、中文新聞標題研究……………......………………………… 20 三、語言計算……………………………...……………………… 23 四、政治新聞研究……………………………...………………… 25 五、小結……………………………………...………………… 26 參、研究方法……………………………….……...…………………… 27 一、研究架構…………………………...…………………………...27 二、研究對象與資料……………………...…………………… 29 肆、研究結果……………………….……...…………………… 49 伍、結論與建議……………………….……...…………………… 70 參考文獻………………………………………………………….…….. 75 附錄…………………………………………………………….………. 81 | |
| dc.language.iso | zh-TW | |
| dc.title | 以機器學習分析線上政治新聞誘餌式標題策略 | zh_TW |
| dc.title | Use Machine Learning to Know How Clickbait Works in Online Political News Headlines | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林麗雲(Lih-Yun Lin),黃瀚萱(Hen-Hsen Huang) | |
| dc.subject.keyword | 政治新聞,誘餌式標題,線上新聞,特徵萃取,邏輯迴歸分類, | zh_TW |
| dc.subject.keyword | Political News,Clickbait,Online News,Feature Selection,Logistic Regression Classifier, | en |
| dc.relation.page | 89 | |
| dc.identifier.doi | 10.6342/NTU201904427 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-03-02 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 新聞研究所 | zh_TW |
| 顯示於系所單位： | 新聞研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-109-1.pdf 目前未授權公開取用 | 3.17 MB | Adobe PDF |
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