Analysing Tweets for Text and Image Features to Detect Fake News Using Ensemble Learning

Meel, Priyanka; Agrawal, Harsh; Agrawal, Mansi; Goyal, Archit

doi:10.1007/978-981-15-0633-8_46

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…• An immediate ban should be placed on all the users, posts and tweets exploiting the COVID-19 context in order to mislead users and disseminate fake news. In this regard, various researchers tackled detecting spams and misleading information in social networks based on users' meta-data, texts and contexts [18]- [33]. However, these approaches did not consider critical and crisis times where high accuracy and time efficiency factors have major impact on overall solutions.…”

Section: Implications and Future Research Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

Mourad

Srour

Harmanai

et al. 2020

IEEE Trans. Netw. Serv. Manage.

118

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News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19's communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users' profiles, meta-data and tweets' context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

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Section: Implications and Future Research Directionsmentioning

confidence: 99%

“…Detecting spammers on social networks most often relies on analyzing the content of messages [9], [29], [31]- [33], [57]. However, most of the approaches extend their techniques by exploiting users' profile and their relations [30].…”

Section: A Spam and Misleading Posts Detectionmentioning

confidence: 99%

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

Mourad

Srour

Harmanai

et al. 2020

IEEE Trans. Netw. Serv. Manage.

118

View full text Add to dashboard Cite

show abstract

“…The major source for the spread of fake news in the present digital era is the advent of social media platforms like Twitter, Facebook, Telegram, and WhatsApp. Then, Meel et al [138] proposed an ensemble learning approach for fake tweet identification. They have incorporated the image as well as textual information associated with the tweet.…”

Section: E Ensemble Learningmentioning

confidence: 99%

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

et al. 2022

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In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and AUC score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.

show abstract

“…• An immediate ban should be placed on all the users, posts and tweets exploiting the COVID-19 context in order to mislead users and disseminate fake news. In this regard, various researchers tackled approached for detecting spams and misleading information in social networks based on users' meta-data, texts and contexts [18]- [33]. However, these approaches did not consider critical and crisis times where high accuracy and time efficiency factors have major impact on overall solutions.…”

Section: Implications and Research Directionsmentioning

confidence: 99%

Section: A Spam and Misleading Posts Detectionmentioning

confidence: 99%

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

Mourad,

Srour,

Harmanani

et al. 2020

Preprint

View full text Add to dashboard Cite

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19's communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This paper presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately 1 million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288, 000 users were analyzed including unique users' profiles, meta-data and tweets' context. The study noted various interesting conclusions including the critical impact of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

show abstract

Analysing Tweets for Text and Image Features to Detect Fake News Using Ensemble Learning

Cited by 8 publications

References 9 publications

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

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