Multi-Source Social Feedback of Online News Feeds
Multi-Source Social Feedback of Online News Feeds
复制标题
在线新闻源的多源社会反馈
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Luís Torgo
中科院分区：
文献类型：
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作者：
Nuno Moniz;Luís Torgo
社交媒体平台崛起带来的大量用户生成内容，使信息检索、推荐系统、数据挖掘和机器学习等领域的研究激增。然而，缺乏全面的基线数据集，无法进行彻底的评价比较，这已成为一个重要问题。在这篇文章中，我们提供了来自著名聚合器如Google News和Yahoo！的大量新闻条目数据集。新闻，以及他们在多个平台上各自的社交反馈：Facebook、Google+和LinkedIn。收集的数据涉及2015年11月至2016年7月期间的8个月，涉及四个不同主题的约10万条新闻：经济、微软、奥巴马和巴勒斯坦。此数据集是为预测分析任务中的评估比较量身定做的，尽管允许执行其他研究领域的任务，如主题检测和跟踪、短文本情感分析、第一个故事检测或新闻推荐。
The profusion of user generated content caused by the rise of social media platforms has enabled a surge in research relating to fields such as information retrieval, recommender systems, data mining and machine learning. However, the lack of comprehensive baseline data sets to allow a thorough evaluative comparison has become an important issue. In this paper we present a large data set of news items from well-known aggregators such as Google News and Yahoo! News, and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The data collected relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation.