A Stochastic Simulation of the Decision to Retweet

@inproceedings{Hochreiter2013ASS,
  title={A Stochastic Simulation of the Decision to Retweet},
  author={Ronald Hochreiter and Christoph Waldhauser},
  booktitle={ADT},
  year={2013}
}
Twitter is a popular microblogging platform that sees a vast increase in use as a marketing communication tool. For any marketing campaign to be successful, word-of-mouth is an essential component. The equivalent of word-of-mouth propagation in Twitter is the retweeting of a message. So far, little focus has been put on how Twitter users arrive at deciding which tweets to retweet and which ones to ignore. This contribution offers a stochastic decision function that models a nodes decision… 

Online Retweet Recommendation with Item Count Limits

  • Xiaoqi ZhaoKeishi Tajima
  • Computer Science
    2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
  • 2014
TLDR
This paper developed a system that reads a sequence of tweets from the friends one by one, and select a given number of tweets in an online (or near-online) fashion, and proposes four algorithms for it that give priority to the timeliness, selection quality, and selection quality.

Retweet Recommendation with Item Count Limits

TLDR
This paper developed a system that reads a sequence of tweets from the friends one by one, and select a given number of tweets in an online (or near-online) fashion, and proposes four algorithms for it that give priority to the timeliness, selection quality, and the selection quality.

The Role of Emotions in Propagating Brands in Social Networks

TLDR
This work investigates how emotions function in social media using more than 30,000 brand marketing messages from the Google+ social networking site and extends upon earlier research by computing multi-level mixed effects models that compare the function of emotions across different industries.

Public Spheres in Twitter- and Blogosphere. Evidence from the US

TLDR
This work looks at the most recent 3,200 tweets that were broadcast from the Republican and Democratic Twitter accounts and extracts the prevailing topics of these tweets and linked websites to find that there is a stark contrast between the behavior of Democratic and Republican followers.

Data Mining Cultural Aspects of Social Media Marketing

TLDR
From an analysis of 400 posts on the localized Google+ pages of German car brands in Germany and the US, it is concluded that posting time and emotions are important predictors for reshare counts.

References

SHOWING 1-10 OF 21 REFERENCES

Why do People Retweet? Anti-Homophily Wins the Day!

TLDR
This work looks to get a better understanding of what makes people spread information in tweets or microblogs through the use of retweeting and finds that, not surprisingly, people's retweeting behavior is better explained through multiple different models rather than one model.

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.

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.

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.

Retweet Modeling Using Conditional Random Fields

TLDR
This paper proposes modeling the retweet patterns using conditional random fields with a three types of user-tweet features: content influence, network influence and temporal decay factor, and demonstrates that CRF can improve prediction effectiveness by incorporating social relationships compared to the baselines that do not.

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.

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.

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.

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.

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.