假新聞中語言特徵的重要性: 透過用詞模式以神經網路實作

Understanding Important Language Features of Fake News: Word Patterns as Neural Networks Inputs

艾洛娜(Shatri, Elona)

指導教授 ： 陳宜欣

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2019年)

摘要

假新聞依據型態，動機或寫作風格有著不同的寫法。先前的假新聞欺騙檢測的相關研究使用人工方式提取特徵，這樣的方法受限於人類自身的語言理解能力。即便這樣，假新聞中內的語言變異特徵的提取還是有其困難。在本研究中，我們探討了使用自動提取重要語義特徵方法的可能性。這些被提取的語意特徵不受限於人類本身的語言理解，同時我們也探討是否這些方法可以捕獲演變中的語言變異性。我們的實驗結果顯示，我們的模型可以與使用傳統機器學習並由人工進行特徵篩選的模型達到相當的效果。

關鍵字

Fake news ； Deception detection ； Automatic extraction ； Linguistic patterns

並列摘要

Fake news articles are differently written, depending on the type, motivation and writing style. Previous work in deception detection in fake news use features that are manually made and are limited to predefined human understandings of linguistics. That being said, it is difficult to extract the shifts in linguistic variability in fake news articles. In this work, we investigate the possibility of using a method that will be able to automatically extract important linguistic-based features. The extracted linguistic-features are not limited to our understandings of linguistics, and we will investigate if they can to capture evolving linguistic variability in fake news. Our experimental results show that our model achieves results that are comparable to the models that use traditional machine learning, which are limited to manual feature selection.

並列關鍵字

假新聞 ； 欺騙檢測 ； 自動提取 ； 語言模式

參考文獻

[1] Fake news: What exactly is it–and how can you spot it? (2019). https://www.telegraph.co.uk/technology/0/fake-news-exactly-has- really-had-influence/.

[2] Collins 2017 word of the year shortlist (2017).

https://www.collinsdictionary.com/word-lovers-blog/new/collins -2017-word-of-the-year-shortlist,396,HCB.html.

[3] Read all about it: The biggest fake news stories of 2016 (2016).

https://www.cnbc.com/2016/12/30/read-all-about-it-the-biggest-fa ke-news-stories-of-2016.html.