Nepotistic relationships in Twitter and their impact on rank prestige algorithms

@article{GayoAvello2013NepotisticRI,
  title={Nepotistic relationships in Twitter and their impact on rank prestige algorithms},
  author={Daniel Gayo-Avello},
  journal={Inf. Process. Manag.},
  year={2013},
  volume={49},
  pages={1250-1280}
}
Finding Correlation Between Twitter Influence Metrics and Centrality Measures for Detection of Influential Users
TLDR
The work presented in this paper reports on the implementation of some centrality measures and Twitter-specific influence measures are applied on a Twitter network of professional wrestling and the correlation among the different measures for detecting influential users.
Leaders in Social Networks, the Delicious Case
TLDR
It is shown that LeaderRank outperforms PageRank in terms of ranking effectiveness, as well as robustness against manipulations and noisy data, which suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.
Lurking in social networks: topology-based analysis and ranking methods
TLDR
This work addresses the new problem of lurker ranking and proposes the first centrality methods specifically conceived for ranking lurkers in social networks, and utilizes only the network topology without probing into text contents or user relationships related to media.
Are Some Tweets More Interesting Than Others? #HardQuestion
TLDR
Crowdourcing was used to assemble a set of tweets rated as interesting or not; these tweets were scored using textual and contextual features; and these scores were used as inputs to a binary classifier, which was able to achieve moderate agreement between the best classifier and the human assessments.
Let’s CoRank: trust of users and tweets on social networks
TLDR
A Coupled Dual Networks Trust Ranking (CoRank) method to evaluate the trustworthiness of users and tweets by analysing user/tweet behaviours on Twitter is developed and compared with three baseline methods PageRank, TURank, and Weighted PageRank to show how the approach outperforms the existing ones.
Measuring node importance on Twitter microblogging
TLDR
The findings suggest that relations of "friendship" at Twitter are important but not enough, and the centrality measures of a node importance do not show how important users are.
A Weighted Multi-factor Algorithm for Microblog Search
TLDR
A weighted multi-factor ranking algorithm (WMFR) is proposed for Twitter search, and it is concluded that the proposed WMFR algorithm is more effective compared to several existing algorithms.
Leveraging Microblogs for Resource Ranking
TLDR
This paper presents a method for resource ranking based on Twitter data structure processing that ranks a microblog user based on his followers count with respect to a number of (re)posts and reflects it into resource ranking.
Discovering Hidden Topical Hubs and Authorities in Online Social Networks
TLDR
A novel topic model known as Hub and Authority Topic (HAT) model is proposed to combine the two process so as to jointly learn the hub, authority and topical interests and it outperforms the state-of-the-art in link recommendation task.
...
...

References

SHOWING 1-10 OF 78 REFERENCES
Measuring User Influence in Twitter: The Million Follower Fallacy
TLDR
An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
What is Twitter, a social network or a news media?
TLDR
This work is the first quantitative study on the entire Twittersphere and information diffusion on it and finds a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
Understanding and combating link farming in the twitter social network
TLDR
It is shown that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the link farming problem in Twitter by disincentivizing users from linking with other users simply to gain influence.
Everyone's an influencer: quantifying influence on twitter
TLDR
It is concluded that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects and that predictions of which particular user or URL will generate large cascades are relatively unreliable.
Detecting spammers on social networks
TLDR
The results show that it is possible to automatically identify the accounts used by spammers, and the analysis was used for take-down efforts in a real-world social network.
De retibus socialibus et legibus momenti
TLDR
A number of techniques to analyze the clicks received by promoted URLs in order to check for any positive correlation between the number of visits and different "influence" scores are compared with a new method firstly described here.
Influence and passivity in social media
TLDR
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.
Study of Trend-Stuffing on Twitter through Text Classification
TLDR
This work studies the use of text-classification over 600 trends consisting of 1.3 million tweets and their associated web pages to identify tweets that are closely-related to a trend as well as unrelated tweets.
A Crow or a Blackbird?: Using True Social Network and Tweeting Behavior to Detect Malicious Entities in Twitter
TLDR
This work uses novel mechanisms that utilize the true social network of users, the quality of information produced by them and their tweeting behavior to identify malicious entities present in Twitter and believes its algorithm is one of the first to automatically deal with a broad range of malicious entities.
Learning influence probabilities in social networks
TLDR
This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.
...
...