A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11 33 . Our source code and dataset are available at this http URL.
, @cite_53 proposed a content-based method to recommend URLs on Twitter. @cite_9 proposed hashtag-based, topic-based and entity-based methods to build user profiles for news personalization. By enriching user profiles with external data sources @cite_40 @cite_15 , , improved URL recommendation results. Taking a similar content-based approach, , @cite_17 employed Twitter lists to recommend fresh URLs and guy2011personalized tried to recommend URLs on streaming data. @cite_4 proposed an SVM based approach to recommend URLs. , @cite_18 exploited Twitter data to discover fresh websites for a web search engine. However, to the best of our knowledge there is no prior work employing matrix factorization models and auxiliary information to recommend URLs to guardians on Twitter. In addition to recommending URLs, researchers also focused on personalizing who to follow @cite_26 , interesting tweets @cite_5 , and Twitter lists @cite_65 .
@cite_18 @cite_26 @cite_4 @cite_9 @cite_53 @cite_65 @cite_40 @cite_5 @cite_15 @cite_17
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Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly. In this paper we introduce the task of "tweet recommendation", the problem of suggesting tweets that match a user's interests and likes. We propose an Information-Retrieval-like model that leverages the content of the user's tweets and those of her friends, and that effectively retrieves a set of tweets that is personalized and varied in nature. Our approach could be easily leveraged to build, for example, a Twitter or Facebook timeline that collects messages that are of interest for the user, but that are not posted by her friends. We compare to typical approaches used in similar tasks, reporting significant gains in terms of overall precision, up to about +20 , on both a corpus-based evaluation and real world user study. More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72 from a baseline of 33 . We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams. We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. We model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter: the profile of the social neighborhood of the users, the content of their own tweet stream, and topic popularity in the news and in the whole twitter-land. We validate our approach on a real-world dataset of approximately 40k articles coming from Yahoo! News and one month of crawled twitter data. We train our model using a learning-to-rank approach and support-vector machines. The train and test set are drawn from Yahoo! toolbar log data. We heuristically identify 3214 users of twitter in the log and use their clicks on news articles to train our system. Our methodology is able to predict with good accuracy the news articles clicked by the users and rank them higher than other news articles. The results show that the combination of various signals from real-time Web and micro-blogging platforms can be a useful resource to understand user behavior.