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Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter
The first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious, is provided.
Social Networks that Matter: Twitter Under the Microscope
A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.
Influence and passivity in social media
An algorithm is proposed that determines the influence and passivity of users based on their information forwarding activity and it is demonstrated that high popularity does not necessarily imply high influence and vice-versa.
Crowdsourcing, attention and productivity
We show through an analysis of a massive data set from YouTube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of…
The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter
Here a formalization and methodology for studying this type of directed closure process is developed, and evidence for its important role in the formation of links on Twitter is provided.
On the Interplay between Social and Topical Structure
The interface of two decisive structures forming the backbone of online social media is examined: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what, and computationally simple structural determinants can provide remarkable performance in both tasks.
Detecting Spam in a Twitter Network
- S. Schoenebeck, Daniel M. Romero, G. Schoenebeck, D. Boyd
- Computer ScienceFirst Monday
- 30 December 2009
This article examines spam around a one-time Twitter meme—“robotpickuplines” and shows the existence of structural network differences between spam accounts and legitimate users, highlighting challenges in disambiguating spammers from legitimate users.
Predicting Reciprocity in Social Networks
- Justin Cheng, Daniel M. Romero, Brendan Meeder, J. Kleinberg
- Computer ScienceIEEE Third Int'l Conference on Privacy, Security…
- 1 October 2011
This paper extracts a network based on directed@-messages sent between users on Twitter, and identifies measures based on the attributes of nodes and their network neighborhoods that can be used to construct good predictors of reciprocity.
Social Networks under Stress
Analysis of a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts finds changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices.
Who Should I Follow? Recommending People in Directed Social Networks
It is found that sharing an audience with someone is a surprisingly compelling reason to follow them, and that similarity is much less persuasive.