Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Fragile online relationship: a first look at unfollow dynamics in twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Twiage: a game for finding good advice on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding factors that affect response rates in twitter
Proceedings of the 23rd ACM conference on Hypertext and social media
Tweets are forever: a large-scale quantitative analysis of deleted tweets
Proceedings of the 2013 conference on Computer supported cooperative work
Investigating the appropriateness of social network question asking as a resource for blind users
Proceedings of the 2013 conference on Computer supported cooperative work
Proceedings of the VLDB Endowment
Understanding the top grass roots in sina-weibo
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Quantifying the invisible audience in social networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analyzing and predicting viral tweets
Proceedings of the 22nd international conference on World Wide Web companion
Are Some Tweets More Interesting Than Others? #HardQuestion
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
Crowd synthesis: extracting categories and clusters from complex data
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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While microblog readers have a wide variety of reactions to the content they see, studies have tended to focus on extremes such as retweeting and unfollowing. To understand the broad continuum of reactions in-between, which are typically not shared publicly, we designed a website that collected the first large corpus of follower ratings on Twitter updates. Using our dataset of over 43,000 voluntary ratings, we find that nearly 36% of the rated tweets are worth reading, 25% are not, and 39% are middling. These results suggest that users tolerate a large amount of less-desired content in their feeds. We find that users value information sharing and random thoughts above me-oriented or presence updates. We also offer insight into evolving social norms, such as lack of context and misuse of @mentions and hashtags. We discuss implications for emerging practice and tool design.