Unsupervised WhatsApp Fake News Detection using Semantic Search

Gaglani, Jaynil; Gandhi, Yash; Gogate, Shubham; Halbe, Aparna

doi:10.1109/iciccs48265.2020.9120902

Cited by 24 publications

(9 citation statements)

References 3 publications

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“…Actors Agents [1], [34], [93], [116] Affiliation [92], [94] Offensive bots [18], [58], [67], [72] Patterns cyborgs [73], [78], [88] copypasta [100] trolls [25], [60], [83], [104] hijacking [49], [69], [106] Deceptive pseudoentities [63], [107], [120] Patterns astroturfing [42], [76] pseudocontent [24], [46], [111], [112] seed-invite-amplify [2], [116] mainstream [34], [39], [92] Evasive gaming heuristics [45] Patterns ML poisoning attack [48], [75] Channels social media [93], [98], [119] web [15], [45] news [7], [129] messaging [28] Target demographic [13],…”

Section: Component Subcomponent Approachesmentioning

confidence: 99%

Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Mirza¹,

Begum²,

Niu³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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Disinformation can be used to sway public opinion toward a certain political or economic direction, adversely impact public health, and mobilize groups to engage in violent disobedience. A major challenge in mitigation is scarcity: disinformation is widespread but its mitigators are few. In this work, we interview fact-checkers, journalists, trust and safety specialists, researchers, and analysts who work in different organizations tackling problematic information across the world. From this interview study, we develop an understanding of the reality of combating disinformation across domains, and we use our findings to derive a cybersecurity-inspired framework to characterize the threat of disinformation. While related work has developed similar frameworks for conducting analyses and assessment, our work is distinct in providing the means to thoroughly consider the attacker side, their tactics and approaches. We demonstrate the applicability of our framework on several examples of recent disinformation campaigns.

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Section: Component Subcomponent Approachesmentioning

confidence: 99%

Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Mirza¹,

Begum²,

Niu³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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show abstract

“…Recent developments regarding Information Retrieval tasks [28,47,48] have demonstrated the potential of combining semantic-aware models along with traditional baseline algorithms (e.g., BM25) [49]. Moreover, the use of semantic-aware models has proven to be an excellent approach to counteract informational disorders (i.e., misinformation, disinformation, malinformation, misleading information, or any other kind of information pollution) [50,51,52,53] or to build automated fact-checking approaches [54]. Additionally, semantic similarity can be applied to organize data according to text properties, which is formally an unsupervised thematic analysis [55].…”

Section: Importance Of Multilingual Semanticsmentioning

confidence: 99%

Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Huertas-García¹,

Martín²,

Huertas‐Tato³

et al. 2022

Preprint

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Both in scientific literature and in industry, semantic and context-aware Natural Language Processingbased solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of 91.58 % ± 2.59 % and 54.65 % ± 32.20 %, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for other highly demanding NLP tasks.

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“…Multiple models were developed for non-English languages as well. Other notable works on fake-news detection and misinformation are [8,19,22].…”

Section: Fake-news Detection In Other Languagesmentioning

confidence: 99%

Multi-Context Based Neural Approach for COVID-19 Fake-News Detection

Desarkar

2022

Companion Proceedings of the Web Conference 2022

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When the world is facing the COVID-19 pandemic, society is also fighting another battle to tackle misinformation. Due to the widespread effect of COVID 19 and increased usage of social media, fake news and rumors about COVID-19 are being spread rapidly. Identifying such misinformation is a challenging and active research problem. The lack of suitable datasets and external world knowledge contribute to the challenges associated with this task. In this paper, we propose MiCNA, a multi-context neural architecture to mitigate the problem of COVID-19 fake news detection. In the proposed model, we leverage the rich information of the three different pre-trained transformer-based models, i.e., BERT, BERTweet and COVID-Twitter-BERT to three different aspects of information (viz. general English language semantics, Tweet semantics, and information related to tweets on COVID 19) which together gives us a single multi-context representation. Our experiments provide evidence that the proposed model outperforms the existing baseline and the candidate models (i.e., three transformer architectures) and becomes a state-of-the-art model on the task of COVID-19 fakenews detection. We achieve new state-of-the-art performance on a benchmark COVID-19 fake-news dataset with 98.78% accuracy on the validation dataset and 98.69% accuracy on the test dataset. CCS CONCEPTS• Computing methodologies → Language resources; Machine learning algorithms.

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Unsupervised WhatsApp Fake News Detection using Semantic Search

Cited by 24 publications

References 3 publications

Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Multi-Context Based Neural Approach for COVID-19 Fake-News Detection

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