Short and tweet: experiments on recommending content from information streams

By Jilin Chen, Rowan Nairn, Les Nelson, Michael S. Bernstein, Ed H. Chi. 2010

479 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “In social media research, the number of posts has been used as a proxy metric for the interest in a topic (Chen et al., 2010); yet, while this number may reﬂect production patterns, it may not reﬂect how much content on the topic users read (as seen in section 5.1).”
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.”
Learning Semantic Relationships between Entities in Twitter (2011, background): “Twitter has become an increasingly popular social network tool, not only for socializing with friends [6], but also for gathering information about what is going on in the world in whatever subject one might be interested in [8,17,7].”
Personalized Recommendation Combining User Interest and Social Circle (2014, unknown): “[43] explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting.”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, background): “(v) What semantic-based information access methods can help address the complex information seeking behavior in social media? For example, semantic search in social media [71] and social media streams recommendation [72].”
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network (2010, background): “In addition, 344 of those 10K tweets had been deleted by users from Twitter later.”
Discovering users' topics of interest on twitter: a first look (2010, background): “, [5] explore the problem of recommending content (Tweets).”
Deep Twitter diving: exploring topical groups in microblogs at scale (2014, background): “There have been several efforts to recommend interesting topical content to Twitter users who are interested in certain top-20 ics [6, 7].”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, background): “Chen et al. [47] also studied content recommendation based on Twitter for better capture users’ attention by exploring three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting.”
We know what @you #tag: does the dual role affect hashtag adoption? (2012, background): “Finally, our work is also related to the recommender systems in microblogging communities, including the recommendation of followee [16], content [7] and conversations [6] in Twitter.”
From chatter to headlines: harnessing the real-time web for personalized news recommendation (2012, background): “Chen et al. [8] propose a URL-recommender system from URLs posted in twitter.”
Analyzing user modeling on twitter for personalized news recommendations (2011, background): “conducted a study on recommending URLs posted in Twitter messages and compare strategies for selecting and ranking URLs by exploiting the social network of a user as well as the general popularity of the URLs in Twitter [7].”
An Empirical Study on Learning to Rank of Tweets (2010, methodology): “Chen et al. (2010) presented an approach to recommend URLs on Twitter as a means to better direct user attention in information streams.”
Recommending twitter users to follow using content and collaborative filtering approaches (2010, methodology): “Perhaps closest to the work in this paper is very recent work by Chen et al [3] on the development and evaluation URL recommendation strategies to demonstrate the utility of using various combinations of tweet content and social graph information during recommendation.”