On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs

Extracting news on specific topics from the Twitter mi-croblogging site poses formidable challenges, which include handling millions of tweets posted daily, judging topicality and importance of tweets, and ensuring trustworthiness of re-sults in the face of spam. To date, all scalable approaches have relied on crowd wisdom, i.e., keyword-matching on the global tweet stream to gather relevant tweets, and crowd-endorsements to judge the importance of tweets. We pro-pose a fundamentally different methodology – for a given topic, we identify trustworthy experts on the topic, and extract news-stories that are most popular among the experts. Com-paring the crowd-based and expert-based methodologies, we demonstrate that the news-stories obtained by our method-ology (i) have higher relevance for a wide variety of top-ics, (ii) achieve very high coverage of important news-stories posted globally in Twitter, and (iii) are far more trustwor-thy. Using our methodology, we implemented and publicly deployed a topical news system for Twitter, which can extract news-stories on thousands of topics