Using Artificial Intelligence Against the Phenomenon of Fake News: A Systematic Literature Review

Al-Asadi, Mustafa A.; Taşdemir, Şakir

doi:10.1007/978-3-030-90087-8_2

Cited by 23 publications

(5 citation statements)

References 51 publications

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“…Surprisingly, the findings reveal that AI acts as both a spreader of fake news and an authoritative agent. On one hand, the power of AI can be harnessed to uncover and identify fake news, potentially aiding users in distinguishing between genuine and fabricated information [340,341].On the other hand, AI can also serve as a harmful agent, amplifying and spreading false or incorrect information, thereby posing a significant challenge in accurately assessing the authenticity of news sources [33,342]. These contradictory findings highlight the complexity and potential pitfalls associated with relying on the sole AI for the analysis of news authenticity.…”

Section: The Role Of Ai: Aid or Pitfall?mentioning

confidence: 99%

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Tomassi,

Falegnami,

Romano

2024

PLoS ONE

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This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

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Section: The Role Of Ai: Aid or Pitfall?mentioning

confidence: 99%

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Tomassi,

Falegnami,

Romano

2024

PLoS ONE

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“…Various areas have used sentiment analysis for the potential benefits of such an analysis. For instance, opinion mining can be used for the identification of customer's opinions [34,35], adopting and formulating better marketing strategies [36], building better recommendation systems [37], developing better healthcare services based on patient's moods and opinions towards disease and drug reactions [38], identification of fake news spread on social media platforms like Twitter [39].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Jatain,

Niranjanamurthy,

Dayananda

2024

SN COMPUT. SCI.

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With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.

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“…The combination of language transformer and N-gram-based ML models in YouTube spam comment detection aims to leverage the strengths of both approaches. The language transformer model can capture the contextualized embeddings of words, while the N-gram model can capture patterns in the frequency and co-occurrence of N-grams, which may be indicative of spammy content (Al-Asadi & Tasdemir, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

DeepGram: Combining Language Transformer and N-Gram based ML Models for YouTube Spam Comment Detection

Agarwal,

Nikitha,

Ramkumar

et al. 2023

JDSIS

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Spam comments on YouTube videos are a persistent issue that can negatively impact the user experience and content creator's reputation. In this paper, we propose an algorithm called "DeepGram" for detecting YouTube spam comments using a combination of deep learning-based language transformer models and N-gram based machine learning models. The algorithm leverages the power of language transformers, which have shown significant success in various natural language processing tasks, along with N-gram based models that capture local context and patterns in the text data. The proposed algorithm goes through several stages, including data collection, text preprocessing, feature extraction, and model training. The collected YouTube comments are preprocessed by removing special characters, punctuation, and HTML tags, and converting them to lowercase. Common stop words are also removed, and stemming or lemmatization is applied to reduce dimensionality. The algorithm then extracts features from the preprocessed comments using a combination of language transformer models and N-gram based features. Finally, the features are fed into machine learning models for training and evaluation. Experimental results on a large dataset of YouTube comments show that the DeepGram algorithm achieves high accuracy and robust performance in detecting spam comments. The proposed algorithm can be potentially employed as an effective tool for YouTube content creators and platform moderators to combat spam comments and improve the quality of user interactions on YouTube videos.

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Using Artificial Intelligence Against the Phenomenon of Fake News: A Systematic Literature Review

Cited by 23 publications

References 51 publications

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

DeepGram: Combining Language Transformer and N-Gram based ML Models for YouTube Spam Comment Detection

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