Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection

Olga Papadopoulou; Giorgos Kordopatis-Zilos; Markos Zampoglou; Symeon Papadopoulos; Yiannis Kompatsiaris

doi:10.18653/v1/S19-2157

Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection

Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris

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Abstract

In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.

Anthology ID:: S19-2157
Volume:: Proceedings of the 13th International Workshop on Semantic Evaluation
Month:: June
Year:: 2019
Address:: Minneapolis, Minnesota, USA
Editors:: Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 924–928
Language:
URL:: https://aclanthology.org/S19-2157/
DOI:: 10.18653/v1/S19-2157
Bibkey:
Cite (ACL):: Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2019. Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 924–928, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection (Papadopoulou et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2157.pdf

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@inproceedings{papadopoulou-etal-2019-brenda,
    title = "Brenda Starr at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Papadopoulou, Olga  and
      Kordopatis-Zilos, Giorgos  and
      Zampoglou, Markos  and
      Papadopoulos, Symeon  and
      Kompatsiaris, Yiannis",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2157/",
    doi = "10.18653/v1/S19-2157",
    pages = "924--928",
    abstract = "In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard."
}

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%0 Conference Proceedings
%T Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection
%A Papadopoulou, Olga
%A Kordopatis-Zilos, Giorgos
%A Zampoglou, Markos
%A Papadopoulos, Symeon
%A Kompatsiaris, Yiannis
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F papadopoulou-etal-2019-brenda
%X In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.
%R 10.18653/v1/S19-2157
%U https://aclanthology.org/S19-2157/
%U https://doi.org/10.18653/v1/S19-2157
%P 924-928

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Markdown (Informal)

[Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection](https://aclanthology.org/S19-2157/) (Papadopoulou et al., SemEval 2019)

Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection (Papadopoulou et al., SemEval 2019)

ACL

Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2019. Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 924–928, Minneapolis, Minnesota, USA. Association for Computational Linguistics.