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Interpretable Features for Distinguishing Machine Generated News Articles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Interpretable Features for Distinguishing Machine Generated News Articles./
作者:	Karumuri, Ravi Teja.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	56 p.
附註:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
標題:	Computer science. -
ISBN:	9798802709351

Interpretable Features for Distinguishing Machine Generated News Articles.
Karumuri, Ravi Teja.

Interpretable Features for Distinguishing Machine Generated News Articles. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 56 p.

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.S.)--Arizona State University, 2022.

This item must not be sold to any third party vendors.

Social media has become a primary means of communication and a prominent source of information about day-to-day happenings in the contemporary world. The rise in the popularity of social media platforms in recent decades has empowered people with an unprecedented level of connectivity. Despite the benefits social media offers, it also comes with disadvantages. A significant downside to staying connected via social media is the susceptibility to falsified information or Fake News. Easy accessibility to social media and lack of truth verification tools favored the miscreants on online platforms to spread false propaganda at scale, ensuing chaos. The spread of misinformation on these platforms ultimately leads to mistrust and social unrest. Consequently, there is a need to counter the spread of misinformation which could otherwise have a detrimental impact on society. A notable example of such a case is the 2019 Covid pandemic misinformation spread, where coordinated misinformation campaigns misled the public on vaccination and health safety.The advancements in Natural Language Processing gave rise to sophisticated language generation models that can generate realistic-looking texts. Although the current Fake News generation process is manual, it is just a matter of time before this process gets automated at scale and generates Neural Fake News using language generation models like the Bidirectional Encoder Representations from Transformers (BERT) and the third generation Generative Pre-trained Transformer (GPT-3). Moreover, given that the current state of fact verification is manual, it calls for an urgent need to develop reliable automated detection tools to counter Neural Fake News generated at scale. Existing tools demonstrate state-of-the-art performance in detecting Neural Fake News but exhibit a black box behavior. Incorporating explainability into the Neural Fake News classification task will build trust and acceptance amongst different communities and decision-makers. Therefore, the current study proposes a new set of interpretable discriminatory features. These features capture statistical and stylistic idiosyncrasies, achieving an accuracy of 82% on Neural Fake News classification. Furthermore, this research investigates essential dependency relations contributing to the classification process. Lastly, the study concludes by providing directions for future research in building explainable tools for Neural Fake News detection.

ISBN: 9798802709351Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Explainable AI

Interpretable Features for Distinguishing Machine Generated News Articles.
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