Detection of Propaganda Using Logistic Regression

Jin-fen, LI; Ye, Zhihao; Xiao, Lu

doi:10.18653/v1/d19-5017

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Emotional appeal is an important strategy used in propaganda techniques (Da San Martino et al, 2020). (Li et al, 2019) found that features of emotions can be good indicators of a propagandistic and nonpropagandistic fragment in news articles. We explore whether emotion features can help in the identification of the type of propaganda technique in the fragment.…”

Section: Embedding Emotion Featurementioning

confidence: 99%

syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection

Jin-fen

Xiao

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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This paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.

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Section: Embedding Emotion Featurementioning

confidence: 99%

syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection

Jin-fen

Xiao

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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“…The combination of linguistic and semantic variables yields an F1 score of 66.16 percent, which is significantly higher than the baseline. [6] Propaganda campaigns attempt to change people's minds in order to further a specific agenda. They take advantage of the Internet's confidentiality, social networks' micro-profiling capabilities, and the ease of organizing and maintaining synchronized networks of records to reach billions of dollars of social network users with persuasive communication that are directly aimed to topics that each individual user is sensitive to, ultimately influencing the outcome on a targeted issue.…”

Section: Related Workmentioning

confidence: 99%

Detection of Textual Propaganda Using Passive Aggressive Classifiers

2023

IJATCSE

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Nowadays, social media activity, particularly news that spreads over the network, is a major source of knowledge. People search out and chew up news from internet-based living because of the low effort, easy access, and rapid dissemination of information. Twitter, as one of the most wellknown continuing news sources, also happens to be one of the most dominant news disseminating media. It has already been known to wreak significant harm by disseminating snippets of gossip. Online clients are typically susceptible, and everything they do on web-based networking media is assumed to be trustworthy. As a result, automating counterfeit propaganda detection is critical to maintaining a vibrant online media and informal organization. In order to computerize propaganda news identification in Twitter datasets, this research develops a technique for recognizing propaganda text messages from tweets by figuring out how to anticipate precision evaluations. This paper proposes a supervised machine learning technique, Passive aggressive classifiers that uses Count Vectorizer and Term FrequencyInverse Document Frequency Vectorizer as feature extraction to detect propaganda news based on the polarity of the corresponding article. Finally, this algorithm uses dataset with 43000 records and shows good accuracy.

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“…Team jinfen (Li et al, 2019) used a logistic regression model fed with a manifold of representations, including TF.IDF and BERT vectors, as well as vocabularies and readability measures.…”

Section: Teams Participating In the Sentence-level Classification Onlymentioning

confidence: 99%

Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

Martino¹,

Barrón-Cedeño²,

Nakov³

2019

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

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We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri. org/nlp4if-shared-task/.

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Detection of Propaganda Using Logistic Regression

Cited by 13 publications

References 21 publications

syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection

syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection

Detection of Textual Propaganda Using Passive Aggressive Classifiers

Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

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