• Corpus ID: 17707364

A Genetic Algorithm to Optimize a Tweet for Retweetability

@article{Hochreiter2014AGA,
  title={A Genetic Algorithm to Optimize a Tweet for Retweetability},
  author={Ronald Hochreiter and Christoph Waldhauser},
  journal={ArXiv},
  year={2014},
  volume={abs/1401.4857}
}
Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize… 

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References

SHOWING 1-10 OF 14 REFERENCES

Why We Twitter: An Analysis of a Microblogging Community

TLDR
It is found that people use microblogging primarily to talk about their daily activities and to seek or share information and that users with similar intentions connect with each other.

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.

Predicting Information Spreading in Twitter

TLDR
A new methodology for predicting the spread of information in a social network based on data of who and what was retweeted and a probabilistic collaborative filter model to predict future retweets is presented.

Information diffusion through blogspace

TLDR
A macroscopic characterization of topic propagation through the authors' corpus is presented, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community.

Maximizing the spread of influence through a social network

TLDR
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

TLDR
It is found that, amongst content features, URLs and hashtags have strong relationships with retweetability and the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, thenumber of past tweets does not predict retweetability of a user's tweet.

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.

Predicting the Speed, Scale, and Range of Information Diffusion in Twitter

TLDR
Results of network analyses of information diffusion on Twitter are presented, via users’ ongoing social interactions as denoted by “@username” mentions, finding that some properties of the tweets themselves predict greater information propagation but that property of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors.

Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior

TLDR
A positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate is found.

Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth

TLDR
The results clearly indicate that information dissemination is dominated by both weak and strong w-o-m, rather than by advertising, which means that strong and weak ties become the main forces propelling growth.