Fake News Detection Regarding the Hong Kong Events from Tweets

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

“…There are several approaches that explore the significance of textual and linguistic features for fake news detection. Nikiforos et al [1] created a novel data set, consisting of 2366 tweets in English, regarding the Hong Kong protests of August 2019. Both network account and linguistic features were extracted from the tweets, while several features were identified as determinant for fake news detection.…”

“…In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Fake news is intentionally forged information, which is distributed either to deceive and make false information believable, or to make verifiable facts doubtful [2,5,[7][8][9][10][11][12]15,[19][20][21].…”

“…Fake news is intentionally forged information, which is distributed either to deceive and make false information believable, or to make verifiable facts doubtful [2,5,[7][8][9][10][11][12]15,[19][20][21]. Propaganda is another relative term for information which promotes specific political motives and other agendas [1,[8][9][10][11][12]16,18,21,22].…”

“…Additionally, grammatical and spelling mistakes, as well as the use of the same limited set of words are characteristic properties of deceptive language [7,11,12,16]. The recent development of natural language processing (NLP), data mining, and machine learning tools has led to a more qualitative understanding of the features of deceptive language (linguistic features), as well as of the features of malicious users and entities (network account features) [1,2,4,5,7,8,11,12,[14][15][16][17][18][19]22].…”

“…Fake news detection is the ability to define the truthfulness of information by analyzing its contents and related features [7,11]. Due to the unstructured and noisy data, the dynamic nature of news, and the increasing number of users, automated solutions for fake news and deception detection in social networks are required [1,2,6,8,10,12,[14][15][16][17][18][19]21,22]. Consequently, fake news and deception detection on social networks present unique challenges and has become an emerging research field, with future directions in data-oriented, feature-oriented, model-oriented, and applicationoriented issues [1][2][3][4][5]8,11,12,15,16,18,22,24,25].…”