Proppy: A System to Unmask Propaganda in Online News

Barrón-Cedeño, Alberto; Martino, Giovanni Da San; Jaradat, Israa; Nakov, Preslav

doi:10.1609/aaai.v33i01.33019847

Cited by 63 publications

(33 citation statements)

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“…Another line of related work focuses on propaganda, which can be seen as a form of extreme bias [13,14,15]. See also a recent position paper [16] and an overview paper on bias on the Web [17].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

et al. 2019

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We address the problem of predicting the leading political ideology, i.e., left-center-right bias, for YouTube channels of news media. Previous work on the problem has focused exclusively on text and on analysis of the language used, topics discussed, sentiment, and the like. In contrast, here we study videos, which yields an interesting multimodal setup. Starting with gold annotations about the leading political ideology of major world news media from Media Bias/Fact Check, we searched on YouTube to find their corresponding channels, and we downloaded a recent sample of videos from each channel. We crawled more than 1,000 YouTube hours along with the corresponding subtitles and metadata, thus producing a new multimodal dataset. We further developed a multimodal deeplearning architecture for the task. Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only. We release the dataset to the research community, hoping to help advance the field of multi-modal political bias detection.

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Section: Related Workmentioning

confidence: 99%

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

et al. 2019

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“…This method was proved inaccurate, as propagan-distic news outlets also publish objective articles in order to gain readers' trust. Barrón-Cedeño et al (2019a); Barrón-Cedeno et al (2019b) designed Proppy, a real time propaganda detection system designed to monitor news sources, which computes a propaganda index using a maximum entropy classifier based on a variety of features including n-grams, readability scores and lexicon features. Baisa et al (2017) introduced a corpus of more than 5,000 Czech newspaper articles annotated for propaganda use, with a large set of features extracted for each one.…”

Section: Related Workmentioning

confidence: 99%

“…This phenomenon was present in the news industry throughout history. However, the concern over the presence of propaganda techniques in news articles has grown exponentially since the rise of social media platforms, especially after the massive impact it had in recent political events, such as the US 2016 elections or Brexit (Barrón-Cedeño et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model

Vlad¹,

Tanase²,

Onose³

et al. 2019

Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaga

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In recent years, the need for communication increased in online social media. Propaganda is a mechanism which was used throughout history to influence public opinion and it is gaining a new dimension with the rising interest of online social media. This paper presents our submission to NLP4IF-2019 Shared Task SLC: Sentence-level Propaganda Detection in news articles. The challenge of this task is to build a robust binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Our model relies on a unified neural network, which consists of several deep leaning modules, namely BERT, BiLSTM and Capsule, to solve the sentencelevel propaganda classification problem. In addition, we take a pre-training approach on a somewhat similar task (i.e., emotion classification) improving results against the coldstart model. Among the 26 participant teams in the NLP4IF-2019 Task SLC, our solution ranked 12th with an F 1-score 0.5868 on the official test data. Our proposed solution indicates promising results since our system significantly exceeds the baseline approach of the task organizers by 0.1521 and is slightly lower than the winning system by 0.0454.

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“…There are a number of researchers applying machine learning to automatically identify Propagandistic news articles. (Barrón-Cedeño et al, 2019) presented PROPPY, the first publicly available real-world, real-time propaganda detection system for online news and they show that character n-grams and other style features outperform existing alternatives to identify propaganda based on word n-grams. (Ahmed et al, 2017) proposed a fake news(propagandistic news articles) detection model that use n-gram analysis and machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Detection of Propaganda Using Logistic Regression

Jin-fen¹,

Ye²,

Xiao³

2019

Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaga

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Various propaganda techniques are used to manipulate peoples perspectives in order to foster a predetermined agenda such as by the use of logical fallacies or appealing to the emotions of the audience. In this paper, we develop a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. We utilize features like TF-IDF, BERT vector, sentence length, readability grade level, emotion feature, LIWC feature and emphatic content feature to help us differentiate these two categories. The linguistic and semantic features combination results in 66.16% of F1 score, which outperforms the baseline hugely.

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Proppy: A System to Unmask Propaganda in Online News

Cited by 63 publications

References 1 publication

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model

Detection of Propaganda Using Logistic Regression

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