Identification of Good and Bad News on Twitter (bibtex)
by Piush Aggarwal and Ahmet Aker
Abstract:
Social media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad labels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.
Reference:
Identification of Good and Bad News on Twitter Piush Aggarwal and Ahmet Aker, In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), INCOMA Ltd., 2019.
Bibtex Entry:
@inproceedings{aggarwal-aker-2019-identification,
    title = "Identification of Good and Bad News on {T}witter",
    author = {Aggarwal, Piush and Aker, Ahmet},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    pages = "9--17",
url = "https://www.aclweb.org/anthology/R19-1002.pdf",
abstract = "Social media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad labels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.",
}