FacTweet: Profiling Fake News Twitter Accounts

Ghanem, Bilal; Ponzetto, Simone Paolo; Rosso, Paolo

doi:10.1007/978-3-030-59430-5_3

Cited by 10 publications

(5 citation statements)

References 20 publications

(32 reference statements)

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“…5W's are What, Who, Where, When, Why, and How. Ghanem et al [34] recommended leveraging suspect accounts' semantic and stylistic characteristics to determine the false integrity of news published by these accounts. Whereas, in [35], the social graphs methodology is used to detect hoaxes over social platforms.…”

Section: Single Modularity Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

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Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNet-B0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

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Section: Single Modularity Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

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“…Ghanem et al. ( 2020 ) proposed an approach to detect non-factual twitter accounts. Shah and Zaman ( 2011 ) proposed a method to estimate the source of rumors using maximum likelihood estimation.…”

Section: Related Workmentioning

confidence: 99%

Empirical evaluation of link deletion methods for limiting information diffusion on social media

Furukawa

Tsugawa

2022

Soc. Netw. Anal. Min.

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Although beneficial information abounds on social media, the dissemination of harmful information such as the so-called fake news has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 10–50% of links from a social network, the size of cascades after link deletion is estimated to be only 50% the original size under the optimistic estimation, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.

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“…The model used CNN to estimate a quantization parameter, intra/inter mode, and deblock setting of pixels patch up in videos to identify and mark the tampered regions in videos. Ghanem et al [33] proposed using the suspicious account's semantic and stylistic features to detect the fake credibility of the news generated from these accounts. On the contrary, Vishwakarma et al [34] proposed web scrapping and image reverse search for fake image detection.…”

Section: Single Modalitymentioning

confidence: 99%

Predicting image credibility in fake news over social media using multi-modal approach

Singh

Sharma

2021

Neural Comput & Applic

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Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

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FacTweet: Profiling Fake News Twitter Accounts

Cited by 10 publications

References 20 publications

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

Empirical evaluation of link deletion methods for limiting information diffusion on social media

Predicting image credibility in fake news over social media using multi-modal approach

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