A Survey of Recommender Systems in Twitter

Kywe, Su Mon; Lim, Ee-Peng; Zhu, Feida

doi:10.1007/978-3-642-35386-4_31

Cited by 60 publications

(36 citation statements)

References 16 publications

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“…In [22] an approach is discussed for recommending users to follow, and the paper provides some useful information on feature selection for training classifiers, identifying mentions, URLs, and hashtags as important. [23] surveys various recommender systems built on Twitter, highlighting only one example [9] which filters a timeline of tweets by predicting their retweet level. More generally, [24] surveys how information is diffused through the Twitter social graph.…”

Section: Related Workmentioning

confidence: 99%

Retweeting beyond expectation: Inferring interestingness in Twitter

Webberley

Allen

Whitaker

2016

Computer Communications

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a b s t r a c tOnline social networks such as Twitter have emerged as an important mechanism for individuals to share information and post user generated content. However, filtering interesting content from the large volume of messages received through Twitter places a significant cognitive burden on users. Motivated by this problem, we develop a new automated mechanism to detect personalised interestingness, and investigate this for Twitter. Instead of undertaking semantic content analysis and matching of tweets, our approach considers the human response to content, in terms of whether the content is sufficiently stimulating to get repeatedly chosen by users for forwarding (retweeting). This approach involves machine learning against features that are relevant to a particular user and their network, to obtain an expected level of retweeting for a user and a tweet. Tweets observed to be above this expected level are classified as interesting. We implement the approach in Twitter and evaluate it using comparative human tweet assessment in two forms: through aggregated assessment using Mechanical Turk, and through a web-based experiment for Twitter users. The results provide confidence that the approach is effective in identifying the more interesting tweets from a user's timeline. This has important implications for reduction of cognitive burden: the results show that timelines can be considerably shortened while maintaining a high degree of confidence that more interesting tweets will be retained. In conclusion we discuss how the technique could be applied to mitigate possible filter bubble effects.

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Section: Related Workmentioning

confidence: 99%

Retweeting beyond expectation: Inferring interestingness in Twitter

Webberley

Allen

Whitaker

2016

Computer Communications

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“…If two users u i ,u k are similar according to some metrics, then we can recommend a j ðu i Þ if a j ðu k Þ is true for u k . In a recent survey (Kywe et al 2012), recommenders are classified in content-based or collaborative filtering approaches. In content-based recommenders, users are represented by the content of their messages (Garcia and Amatriain 2010;Lu et al 0000).…”

Section: Twitter Recommender Systemsmentioning

confidence: 99%

“…In Kywe et al (2012), a variety of useful recommendation tasks are surveyed, e.g., re-tweeting, who to follow, who should be followed, including a hashtag, a mention or an url in a tweet, reading a tweet, etc. Who to follow (WTF) is by far and away the most popular recommendation task.…”

Section: Characterization Of the Tasksmentioning

confidence: 99%

Recommendation of microblog users based on hierarchical interest profiles

Faralli

Stilo

Velardi

2015

Soc. Netw. Anal. Min.

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Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based, either on the user's network structure, or on some notion of topical similarity with other users, or on both. In this paper, we propose to accomplish the recommendation task in two steps: First, we profile users and classify them as belonging to a target community (depending e.g., on their political affiliation, preferred football team, favorite coffee shop, etc.). Then, we fine-tune recommendations for selected populations. We cast both problems of user classification and recommendation as one of itemset mining, where items are either users' authoritative friends or semantic categories associated to friends, extracted from WiBi, the Wikipedia Bitaxonomy. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very finegrained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.

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“…Recommendation techniques have been applied to personalize the streams in online social networks such as Facebook, Google+ and Twitter [22,23]. Facebook's edge rank algorithm is one such filtering technique which presents a personalized stream of news and friends' status updates to the user by ranking every interaction on the site [24].…”

Section: Related Workmentioning

confidence: 99%

Understanding and controlling the filter bubble through interactive visualization

Nagulendra

Vassileva

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.

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A Survey of Recommender Systems in Twitter

Cited by 60 publications

References 16 publications

Retweeting beyond expectation: Inferring interestingness in Twitter

Retweeting beyond expectation: Inferring interestingness in Twitter

Recommendation of microblog users based on hierarchical interest profiles

Understanding and controlling the filter bubble through interactive visualization

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