Trends in combating fake news on social media – a survey

Abstract

cial media following its introduction has witnessed a lot of scholarly attention in recent years due to its growing popularity. These various social media sites have become the mecca of information because of their less costly and easy accessibility. Although these sites were developed to enhance our lives, they are seen as both angelic and vicious. Growing misinformation and fake content by malicious users have not only plagued our online social media ecosystem into chaos, but it also meted untold suffering to humankind. Recently, social media has witnessed a reverberation amid the proliferation of fake news which has made people reluctant to engage in genuine news sharing for fear that such information is false. Consequently, there is a dire need for these fake content to be detected and removed from social media. This study explores the various methods of combating fake news on social media such as Natural Language Processing, Hybrid model. We surmised that detecting fake news is a challenging and complex issue, however, it remains a workable task. Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.

Mapping Disinformation During the Covid-19 in Indonesia: Qualitative Content Analysis

“…However, these numbers can give a glimpse into a situation where rumors or hoaxes are intoxicating the public social media timeline. So, this study's results are in line with Collins et al (2020) that one of the potentials is to spread disinformation through social media. Rumors that spread fear and uncertainty circulated massively, especially among the surrounding communities.…”

Content-based fake news classification through modified voting ensemble

“…The authors describe that fake news can be present in clickbait, propaganda, satire and parody, hoaxes, and others (mainly name-theft, framing, and journalistic deception). Collins et al (2020b) presents an overview of the various models for fake news detection, including machine learning, Natural Language Processing (NLP), expert fact-checking, and hybrid expert machines. Tran et al (2020) utilizes context-based detection techniques for fake news from Twitter posts.…”

Evaluation of Machine Learning Methods for Fake News Detection

“…The interested reader may consult the following articles [16][17][18][19][20][21][22] for complete surveys on articles related to fake news detection.…”