SemEval-2019 Task 4: Hyperpartisan News Detection

Kiesel, Johannes; Mestre, Maria; Shukla, Rishabh; Vincent, Emmanuel; Adineh, Payam; Corney, David; Stein, Benno; Potthast, Martin

doi:10.18653/v1/s19-2145

Cited by 167 publications

(194 citation statements)

References 42 publications

(17 reference statements)

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“…This report describes an experiment based on these starting points, performed on data from the 2019 SemEval task on Hyperpartisan News Detection. (Kiesel et al, 2019) 2 Gavagai Explorer…”

Section: Hyperpartisanismmentioning

confidence: 99%

Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

Afsarmanesh¹,

Karlgren²,

Sumbler³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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This report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items carried out by the Harry Friberg team from Gavagai. We used manually crafted metatopics, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general characteristics of hyperpartisan news items. While the precision of the resulting effort is less than stellar-our contribution ranked 37th of the 42 successfully submitted experiments with overly high recall (95%) and low precision (54%)-we believe we have a model which allows us to continue exploring the underlying features of what the subgenre of hyperpartisan news items is characterised by.

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“…This report describes an experiment based on these starting points, performed on data from the 2019 SemEval task on Hyperpartisan News Detection. (Kiesel et al, 2019) 2 Gavagai Explorer…”

Section: Hyperpartisanismmentioning

confidence: 99%

Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

Afsarmanesh¹,

Karlgren²,

Sumbler³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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“…SemEval Task 4 (Kiesel et al, 2019) tasked participating teams with identifying news articles that are misleading to their readers, a phenomenon often associated with "fake news" distributed by partisan sources (Potthast et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector

Drissi¹,

Segura²,

Ojha³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model (Devlin et al., 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from Se-mEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our bestperforming model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.

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“…Hyperpartisan news detection (Kiesel et al, 2019;Potthast et al, 2018) is a binary classification task, in which given a news article text, systems have to decide whether or not it follows a hyperpartisan argumentation, i.e., "whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person" (2019). As resources for building such a system, the by-publisher and by-article datasets are provided by the organizer.…”

Section: Introductionmentioning

confidence: 99%

The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

Hanawa

Sasaki²,

Ouchi

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection. Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from the by-publisher dataset. Our system achieved 80.9% accuracy on the test set for the formal run and got the 3rd place out of 42 teams.

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SemEval-2019 Task 4: Hyperpartisan News Detection

Cited by 167 publications

References 42 publications

Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector

The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

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