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Title A CONCEPTUAL MODEL FOR EARLY DETECTION OF FAKE NEWS

Author(s) Tasbeel Ahmad

Abstract In the era of technology and digital media, the stormy interaction and massive spread of information have increased the significance of the need for credible information. The concept of fake news or forged news in that regard is not new and its ultimate profound impact on the addressed audience. This malicious act causes discomfort, character assassination, privacy breach, and defamation of the targeted audience. Such news is endorsed to disrupt society’s normal functioning. Fake news due to its persuading terminologies and factors tends to destroy the openness to truth seeing. It interrupts the normal thinking process of the targeted audience and they end up having a typical or tuned mindset which ignites violence in society. Reviewed research depicts that automated detection of fake news has always been the prime focus whose authenticity according to the researchers’ community, however, is still questionable. It is important to understand that automation without unfolding the core constructs based on which news is labeled as fake can never be relied as the pattern of news dispersion and creation changes with time or invention in technology. Moreover, manual detection has correspondingly added value to the existing research in terms of the detection of fake news. However, it is considered a costly and tiresome task. It is also notable that the present research is ignoring the fact that what makes news a fake news. The need of the hour is to make an effort to carry the focus to the constructs contributing or labeling to the detection of fake news at early stages based on the previous and recent state of knowledge. Furthermore, a conceptual model to standardize the detection process based on verified contributing core constructs needs to be developed. The objective of this research is to identify the constructs, classify and categorize news for the detection of fake news. Thus, this research contributes a conceptual model encompassing different core constructs contributing to the early detection of fake news from the point it originates and disperses. On that account, a systematic literature review methodology is conducted to extract constructs from existing literature along with implicit and explicit removal. Subsequently, the data coding technique of grounded theory is applied for encoding the extracted data. Lastly, expert reviews have been conducted for the validation of that proposed conceptual model encompassing core constructs contributing to the propagation and dispersion of fake news. Resultantly, a total of 74 constructs are identified which are further grouped into 15 categories. This research will eventually help data-scientist to label the news as fake or real based upon the recognized, verified constructs.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2021-09-24

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