论文标题
偏见在新闻推荐中的作用在COVID-19的感知中
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic
论文作者
论文摘要
新闻推荐系统（NRS）已被证明可以塑造公共话语，并实施对民主政治产生批判性和有害影响的行为。早期对媒体偏见影响的研究揭示了它们对观点和偏好的强烈影响。负责的NR一旦超出了精确度措施，就应该具有去极化能力。我们通过使用BERT4REC算法来研究覆盖范围和用户行为的新闻相互作用进行了序列预测。根据一个新闻媒体“事件爆发”的实时数据和大型数据集的培训，在数据科学和心理学之间的跨学科方法中研究了围绕旗帜和“滤泡”效果的集会和“滤泡”。通过培训模型，具有大量的文章，关键字和用户行为的培训，可以概述超出准确度措施的公平NR的潜力。追溯到Covid-19的新闻报道和用户行为的发展，主要是医学到更广泛的政治内容和辩论。我们的研究为未来开发负责任的新闻建议提供了首先见解，这些新闻建议在刺激多样性和问责制的同时而不是准确性，而不是准确。
News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet "event bursts", "rally around the flag" effect and "filter bubbles" were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.
