From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism
From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism
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从人群评级到新闻价值预测模型以支持科学新闻
DOI：
10.1145/3555542
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发表时间：
2022
影响因子：
--
通讯作者：
Diakopoulos, Nicholas
中科院分区：
文献类型：
--
作者：
Nishal, Sachita;Diakopoulos, Nicholas
科学出版物的规模持续增长，给科学记者带来了超负荷的负担，他们被淹没在选择中，寻找最有趣、最重要和最有新闻价值的报道。我们的工作通过考虑创建一个科学文章新闻价值预测模型的可行性来解决这个问题，该模型使用众包的新闻价值评估进行训练。我们首先通过评估新闻价值的众包评估的潜力进行评估，通过评估它们与新闻价值的专家评级的一致性，分析定量相关性和定性评级的基本原理，以了解限制。然后，我们展示并评估了一个预测模型，该模型是在这些人群评分以及arXiv文章元数据、文本和其他计算特征上训练的。基于我们开发的众包协议，我们发现，虽然众包的新闻价值评级往往与专家评级适度一致，但也存在显着的差异和分歧，限制了这种方法。然而，尽管有这些限制，我们也发现，我们建立的预测模型在与专家评估（P@10 = 0.8，P@15 = 0.67）进行验证时，提供了一组相当精确的排名，这表明可以从新闻价值的众包评估中学习到一个可行的信号。基于这些发现，我们讨论了未来工作的机会，利用众包和预测方法来支持新闻工作，发现和过滤有新闻价值的信息。
The scale of scientific publishing continues to grow, creating overload on science journalists who are inundated with choices for what would be most interesting, important, and newsworthy to cover in their reporting. Our work addresses this problem by considering the viability of creating a predictive model of newsworthiness of scientific articles that is trained using crowdsourced evaluations of newsworthiness. We proceed by first evaluating the potential of crowd-sourced evaluations of newsworthiness by assessing their alignment with expert ratings of newsworthiness, analyzing both quantitative correlations and qualitative rating rationale to understand limitations. We then demonstrate and evaluate a predictive model trained on these crowd ratings together with arXiv article metadata, text, and other computed features. Based on the crowdsourcing protocol we developed, we find that while crowdsourced ratings of newsworthiness often align moderately with expert ratings, there are also notable differences and divergences which limit the approach. Yet despite these limitations we also find that the predictive model we built provides a reasonably precise set of rankings when validated against expert evaluations (P@10 = 0.8, P@15 = 0.67), suggesting that a viable signal can be learned from crowdsourced evaluations of newsworthiness. Based on these findings we discuss opportunities for future work to leverage crowdsourcing and predictive approaches to support journalistic work in discovering and filtering newsworthy information.
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DOI：
--
发表时间：
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期刊：
Routledge Handbook of Public Communication of Science and Technology
影响因子：
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作者：
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影响因子：
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