Detecting Propaganda Techniques in Memes

Dimitrov, Dimitar; Ali, Bishr Bin; Shaar, Shaden; Alam, Firoj; Silvestri, Fabrizio; Firooz, Hamed; Nakov, Preslav; Martino, Giovanni Da San

doi:10.18653/v1/2021.acl-long.516

Cited by 40 publications

(35 citation statements)

References 28 publications

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“…Loaded Language Loaded language is another propaganda technique that utilizes emotiontriggering terms or phrases to influence audiences' opinions Dimitrov et al, 2021) Some generated examples for appeal to authority and loaded language are shown in Table 2. Upon collecting the training data for generating loaded language, we fine-tune another BART (Lewis et al, 2020) on this dataset.…”

Section: Propaganda Generationmentioning

confidence: 99%

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

Huang¹,

McKeown²,

Nakov³

et al. 2022

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While there has been a lot of research and many recent advances in neural fake news detection, defending against human-written disinformation remains underexplored. Upon analyzing current approaches for fake news generation and human-crafted articles, we found that there is a gap between them, which can explain the poor performance on detecting human-written fake news for detectors trained on automatically generated data. To address this issue, we propose a novel framework for generating articles closer to human-written ones. Specifically, we perform self-critical sequence training with natural language inference to ensure the validity of the generated articles. We then explicitly incorporate propaganda techniques into the generated articles to mimic how humans craft fake news. Eventually, we create a fake news detection training dataset, PROPANEWS, which includes 2,256 examples. Our experimental results show that detectors trained on PROPANEWS are 7.3% to 12.0% more accurate for detecting humanwritten disinformation than for counterparts trained on data generated by state-of-the-art approaches.

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Section: Propaganda Generationmentioning

confidence: 99%

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

Huang¹,

McKeown²,

Nakov³

et al. 2022

Preprint

Self Cite

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“…The rise in misconduct on social media is a prominent research topic. Some forms of online misconduct include rumours (Zhou et al, 2019), fake news (Aldwairi and Alwahedi, 2018;Shu et al, 2017;Nguyen et al, 2020), misinformation (Ribeiro et al, 2021;Shaar et al, 2022), disinformation (Alam et al, 2021;Hardalov et al, 2022), hate speech (MacAvaney et al, 2019;Zhang and Luo, 2019;Zampieri et al, 2020), trolling (Cook et al, 2018), and cyber-bullying (Kowalski et al, 2014;. Some notable work in this direction includes stance (Graells-Garrido et al, 2020) and rumour veracity prediction, in a multi-task learning framework (Kumar and Carley, 2019), wherein the authors proposed a Tree LSTM for characterizing online conversations.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al (2021) argued that online attention can be garnered immensely via fauxtography, which could eventually evolve towards turning into memes that potentially go viral. To support research on these topics, several datasets for offensiveness, hate speech, and harmfulness detection have been created (Suryawanshi et al, 2020;Kiela et al, 2020;Pramanick et al, 2021a,b;Gomez et al, 2020;Dimitrov et al, 2021;Sharma et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

DISARM: Detecting the Victims Targeted by Harmful Memes

Sharma¹,

Akhtar²,

Nakov³

et al. 2022

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Internet memes have emerged as an increasingly popular means of communication on the Web. Although typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultural, and psychological grounds. While previous work has focused on detecting harmful, hateful, and offensive memes, identifying whom they attack remains a challenging and underexplored area. Here we aim to bridge this gap. In particular, we create a dataset where we annotate each meme with its victim(s) such as the name of the targeted person(s), organization(s), and community(ies). We then propose DISARM (Detecting vIctimS targeted by hARmful Memes), a framework that uses named entity recognition and person identification to detect all entities a meme is referring to, and then, incorporates a novel contextualized multimodal deep neural network to classify whether the meme intends to harm these entities. We perform several systematic experiments on three test setups, corresponding to entities that are (a) all seen while training, (b) not seen as a harmful target on training, and (c) not seen at all on training. The evaluation results show that DISARM significantly outperforms ten unimodal and multimodal systems. Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.

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“…Reis et al ( 2020) built a dataset of memes related to the 2018 and the 2019 election in Brazil (34k images, 17k users) and India (810k images, 63k users) with focus on misinformation. Another dataset of 950 memes targeted the propaganda techniques used in memes (Dimitrov et al, 2021a), which was also featured as a shared that at SemEval-2021 (Dimitrov et al, 2021b). Leskovec et al (2009) introduced a dataset of 96 million memes collected from various links and blog posts between August 2008 and April 2009 for tracking the most frequently appearing stories, phrases, and information.…”

Section: Related Workmentioning

confidence: 99%

Detecting Harmful Memes and Their Targets

Pramanick¹,

Dimitrov²,

Mukherjee³

et al. 2021

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Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions. Although memes are typically humorous in nature, recent days have witnessed a proliferation of harmful memes targeted to abuse various social entities. As most harmful memes are highly satirical and abstruse without appropriate contexts, off-the-shelf multimodal models may not be adequate to understand their underlying semantics. In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target. To this end, we present HarMeme, the first benchmark dataset, containing 3, 544 memes related to COVID-19. Each meme went through a rigorous two-stage annotation process. In the first stage, we labeled a meme as very harmful, partially harmful, or harmless; in the second stage, we further annotated the type of target(s) that each harmful meme points to: individual, organization, community, or society/general public/other. The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks. We further discuss the limitations of these models and we argue that more research is needed to address these problems.

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Detecting Propaganda Techniques in Memes

Cited by 40 publications

References 28 publications

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

DISARM: Detecting the Victims Targeted by Harmful Memes

Detecting Harmful Memes and Their Targets

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