A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections

Reis, Júlio C. S.; Melo, Philipe; Garimella, Kiran; Almeida, Jussara M.; Eckles, Dean; Benevenuto, Fabrí­cio

doi:10.1609/icwsm.v14i1.7356

Cited by 31 publications

(19 citation statements)

References 14 publications

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“…This data can also supplement other datasets on misinformation. (Reis et al 2020) summarize datasets that are focused on the automation of fake news detection.…”

Section: Discussionmentioning

confidence: 99%

“…(Nakamura, Levy, and Wang 2020) provide a dataset of one million multi-modal posts from Reddit, classified into different types of misinformation. (Reis et al 2020) provide a dataset of Brazilian and Indian election related images circulated on WhatsApp, that were found to be misinformation. (Gupta et al 2013) analyze fake images on Twitter during Hurricane Sandy.…”

Section: Characterizing Check-worthinessmentioning

confidence: 99%

“…Since a lot of misinformation research has focused on English content, we also highlight here work that focuses on non-English media. The dataset provided by (Reis et al 2020) has images with content in Portugese and Indian languages. (Abonizio et al 2020) look at language features across English, Spanish and Portugese for fake news detection.…”

Section: Characterizing Check-worthinessmentioning

confidence: 99%

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Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking

Prabhakar¹,

Gupta²,

Nadig³

et al. 2021

ICWSM

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Volume of content and misinformation on social media is rapidly increasing. There is a need for systems that can support fact checkers by prioritizing content that needs to be fact checked. Prior research on prioritizing content for fact-checking has focused on news media articles, predominantly in English language. Increasingly, misinformation is found in user-generated content. In this paper we present a novel dataset that can be used to prioritize check-worthy posts from multi-media content in Hindi. It is unique in its 1) focus on user generated content, 2) language and 3) accommodation of multi-modality in social media posts. In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation. The data is accessible on Zenodo (https://zenodo.org/record/4032629) under Creative Commons Attribution License (CC BY 4.0).

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“…This data can also supplement other datasets on misinformation. (Reis et al 2020) summarize datasets that are focused on the automation of fake news detection.…”

Section: Discussionmentioning

confidence: 99%

Section: Characterizing Check-worthinessmentioning

confidence: 99%

Section: Characterizing Check-worthinessmentioning

confidence: 99%

See 1 more Smart Citation

Check Mate: Prioritizing User Generated Multi-Media Content for Fact-Checking

Prabhakar¹,

Gupta²,

Nadig³

et al. 2021

ICWSM

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“…In (Moreira et al 2016), authors introduced a non-deep learning based, temporally robust feature extractor and a bag of visual words method to classify pornographic videos with considerable accuracy. Content modified images, which skew perceptions of viewers (Reis et al 2020), and unfair classifiers, which are biased to different target populations (Kyriakou et al 2019) are also topics concerning social media image analytics.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the use of content filtering algorithms, social media is plagued with fake audio-visual or text content, cyberbullying (Vishwamitra et al 2021), as well as pornographic adult content due to the inability to screen content across social media platforms. For example, the spread of fake images generated for misinformation campaigns that were recently found in Brazil and India used messaging platforms such as Whatsapp (Reis et al 2020) which are difficult to regulate. Visual analytic systems for investigating misinformation are driven by multimodal decision-making AI algorithms which scrub the internet for textual and image-based data (Karduni et al 2018), which work well for platforms in which they can be deployed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks

Silva

Das

Aladdini

et al. 2022

ICWSM

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Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.

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