Using twitter to recommend real-time topical news

By Owen Phelan, Kevin McCarthy, Barry Smyth. 2009

470 citations recorded by the source corpus.

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a user's favourite RSS feeds. A preliminary evaluation is carried out on an implementation of this technique that shows promising results.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “To alleviate the impact of information over-loading, it is critical to help users target their reading interests and make personalized recommendations [2, 25, 27, 32, 34, 39].”
Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, background): “Examples of text recommendations include recommending blogs, social media posts [1], news articles [2, 3], movies (based on plot summaries), products (based on reviews) [4] and research papers [5].”
Predicting the Political Alignment of Twitter Users (2011, background): “events [9], and the identification of breaking news stories [10]– [12].”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, unknown): “…reactions of the users to events (Benhardus & Kalita, 2013; Bifet & Frank, 2010; Hsieh, Moghbel, Fang, & Cho, 2013; Mathioudakis & Koudas, 2010; Nav-eed, Gottron, Kunegis, & Alhadi, 2011; Petrovic, Osborne, & Lav-renko, 2010; Phelan, McCarthy, & Smyth, 2009; Thelwall, Buckley, & Paltoglou, 2011).”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Content-based news recommendation systems [52] , [53] require the topical analysis of news articles to recommend interesting news for users.”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, background): “[46] identified emerging topics of interest from Twitter information using topic modeling, and recommended news by matching emerging topics and recent news coverage in an RSS feed.”
Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (2011, methodology): “This paper extends some of the previous work presented in [15], which described an early prototype of the Buzzer system, in two ways.”
Personalized Recommendation Combining User Interest and Social Circle (2014, unknown): “[42] proposed a news recommendation technique utilizing real-time Twitter data as the basis for ranking and recommending articles from a collection of really simple syndication feeds.”
Personalized News Recommendation Using Twitter (2013, background): “Phelan et al. focus on using micro-blogging activity to recommend news stories [7].”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “While researchers have considered tweet recommendation systems [43, 44, 21, 1, 8], it remains a nontrivial challenge to find the right tweet for the right person at the right time.”
A biterm topic model for short texts (2013, methodology): “With the emergence of social media in recent years, topic models have been utilized for social media content analysis in various tasks, such as content characterizing [26, 35], event tracking [20], content recommendation [25, 8], and in-ﬂuential users prediction [32].”
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (2017, background): “as 4G, mobile news portals or news feed services including social media posts [17] and news articles [6, 34] have gained signicant”
BTM: Topic Modeling over Short Texts (2014, methodology): “With the emergence of social media in recent years, topic models have been utilized for social media content analysis in various tasks, such as content characterizing [1], [2], event tracking [5], content recommendation [24], [25], and influential users prediction [4].”
Neural News Recommendation with Long- and Short-term User Representations (2019, background): “Many conventional news recommendation methods rely on manual feature engineering to build news and user representations (Phelan et al., 2009; Liu et al., 2010; Li et al., 2010; Son et al., 2013; Li et al., 2014; Bansal et al., 2015; Lian et al., 2018).”
A Little Bird Told Me, So I Didn't Believe It: Twitter, Credibility, and Issue Perceptions (2012, background): “In a study of health messages, Hu and Sundar (2010) considered both the originating source (e.”
NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “ly suffer from the sparsity and the cold-start problems, which are especially significant in news recommendation scenarios [19]. Thus, content-based techniques are usually complementary methods to CF [2, 20, 21, 24, 27, 29, 32, 39]. For example, Liu et al. [21] proposed to incorporate user interests”
Hip and trendy: Characterizing emerging trends on Twitter (2011, methodology): “This simple approach is similar to those used in other efforts (Nagarajan et al., 2009) and, as noted by Phelan, McCarthy, and Smyth (2009), it “does serve to provide a straightforward and justiﬁable starting point.””