A manual audit of nearly 4,000 text, speech, and video datasets finds AI training data increasingly comes from web and social media sources, carries hidden non-commercial restrictions, and remains Western-centric with no relative diversity gains since 2013.
Multi-Source Social Feedback of Online News Feeds
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abstract
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.
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Bridging the Data Provenance Gap Across Text, Speech and Video
A manual audit of nearly 4,000 text, speech, and video datasets finds AI training data increasingly comes from web and social media sources, carries hidden non-commercial restrictions, and remains Western-centric with no relative diversity gains since 2013.