Detecting Toxicity in News Articles: Application to Bulgarian

Dinkov, Yoan; Koychev, Ivan; Nakov, Preslav

doi:10.26615/978-954-452-056-4_029

Cited by 11 publications

(4 citation statements)

References 59 publications

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“…This includes studying the impact of misinformation on politics [4], [15], [16], society [5], [8], economy [7], and health [18]. Different machine learning algorithms have been developed to automatically detect false news [34]- [37], propaganda [26], [38], [39], toxicity [40], and fact checking [41] from the online web content.…”

Section: Related Workmentioning

confidence: 99%

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ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

2020

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Today, the rapid dissemination of information on digital platforms has seen the emergence of information pollution such as misinformation, disinformation, fake news, and different types of propaganda. Information pollution has become a serious threat to the online digital world and has posed several challenges to social media platforms and governments around the world. In this paper, we propose Propaganda Spotting in Online Urdu Language (ProSOUL)-a framework to identify content and sources of propaganda spread in the Urdu language. First, we develop a labelled dataset of 11,574 Urdu news to train the machine learning classifiers. Next, we develop the Linguistic Inquiry and Word Count (LIWC) dictionary to extract psycho-linguistic features of Urdu text. We evaluate the performance of different classifiers by varying n-gram, News Landscape (NELA), Word2Vec, and Bidirectional Encoder Representations from Transformers (BERT) features. Our results show that the combination of NELA, word n-gram, and character n-gram features outperform with 0.91 accuracy for Urdu text classification. In addition, Word2Vec embedding outperforms BERT features in classification of the Urdu text with 0.87 accuracy. Moreover, we develop and classify large scale Urdu content repositories to identify web sources spreading propaganda. Our results show that ProSOUL framework performs best for propaganda detection in the online Urdu news content compared to the general web content. To the best of our knowledge, this is the first study on the detection of propaganda content in the Urdu language.

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Section: Related Workmentioning

confidence: 99%

“…To evade the problem, English language resources and datasets were translated to low resource languages. For instance, news toxicity detector was developed by translating English news text to Bulgarian language [40]. The toxicity detector showed the accuracy of 59% with stylometric, NELA, and word embedding features.…”

Section: Related Workmentioning

confidence: 99%

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

2020

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“…While the analysis of the language used by the target news outlet is the most important information source, we can also consider information in Wikipedia and in social media, traffic statistics, and the structure of the target sites URL as shown in Figure 1: 1. the text of a few hundred articles published by the target news outlet, analyzing the style, subjectivity, sentiment, offensiveness [77,78,79,62], toxicity [30], morality, vocabulary richness, propagandistic content, etc. ; 2. the text of its Wikipedia page (if any), including infobox, summary, content, categories, e.g., it might say that the website spreads false information and conspiracy theories; 3. metadata and statistics about its Twitter account (if any): is it an old account, is it verified, is it popular, how is the medium self-describing, is there a link to its website, etc.…”

Section: Related Workmentioning

confidence: 99%

Can We Spot the "Fake News" Before It Was Even Written?

Nakov¹

2020

Preprint

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Given the recent proliferation of disinformation online, there has been also growing research interest in automatically debunking rumors, false claims, and "fake news." A number of fact-checking initiatives have been launched so far, both manual and automatic, but the whole enterprise remains in a state of crisis: by the time a claim is finally factchecked, it could have reached millions of users, and the harm caused could hardly be undone. An arguably more promising direction is to focus on fact-checking entire news outlets, which can be done in advance. Then, we could fact-check the news before it was even written: by checking how trustworthy the outlets that published it is. We describe how we do this in the Tanbih news aggregator, which makes readers aware of what they are reading. In particular, we develop media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics.

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“…It is also useful to understand whether the post is propagandistic, what propaganda techniques are used, and how the issue is framed. While there have been studies focusing on (some of) these issues for high-resource languages such as English and Arabic (Barrón-Cedeño et al, 2020;Hossain et al, 2020;Li et al, 2020;Nakov et al, 2021a,c), there has been less work for low-resource languages such as Bulgarian (Dinkov et al, 2019;Alam et al, 2021d;Shaar et al, 2021b,c). Here, we aim to bridge this gap by analyzing tweets and Facebook posts about COVID-19 in Bulgarian, with focus on factuality, harmfulness, propaganda, and framing.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing

Nakov¹,

Alam²,

Shaar³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic is currently ranked very high on the list of priorities of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. With this in mind, we studied how COVID-19 is discussed in Bulgarian social media in terms of factuality, harmfulness, propaganda, and framing. We found that most Bulgarian tweets contain verifiable factual claims, are factually true, are of potential public interest, are not harmful, and are too trivial to fact-check; moreover, zooming into harmful tweets, we found that they spread not only rumors but also panic. We further analyzed articles shared in Bulgarian partisan pro/con-COVID-19 Facebook groups and found that propaganda is more prevalent in skeptical articles, which use doubt, flag waving, and slogans to convey their message; in contrast, concerned ones appeal to emotions, fear, and authority; moreover, skeptical articles frame the issue as one of quality of life, policy, legality, economy, and politics, while concerned articles focus on health & safety.

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Detecting Toxicity in News Articles: Application to Bulgarian

Cited by 11 publications

References 59 publications

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

Can We Spot the "Fake News" Before It Was Even Written?

COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing

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