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Reply trees in Twitter: data analysis and branching process models

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TLDR
It is suggested that the in-degree of the tweet that initiates a reply tree may play an important role in forming the global shape of the reply tree.
Abstract
Structure of networks constructed from mentioning relationships between posts in online media may be valuable for understanding how information and opinions spread in these media We crawled Twitter to collect tweets and replies to construct a large number of so-called reply trees, each of which was rooted at a tweet and joined by replies Consistent with the previous literature, we found that the empirical trees were characterized by some long path-like reply trees, large star-like trees, and long irregular trees, although their frequencies were not high We tested several branching process models to explain the empirical frequency of these types of reply trees as well as more basic quantities such as the distributions of the size and depth of the reply tree Based on our modeling results, we suggest that the in-degree of the tweet that initiates a reply tree (ie, the number of times that the tweet is directly mentioned by other reply posts) may play an important role in forming the global shape of the reply tree

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Citations
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What is Twitter

Journal ArticleDOI

An overview of online fake news: Characterization, detection, and discussion

TL;DR: A comprehensive overview of the finding to date relating to fake news is presented, characterized the negative impact of online fake news, and the state-of-the-art in detection methods are characterized.
Journal ArticleDOI

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

TL;DR: An effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech is presented.
Book ChapterDOI

The Anatomy of Reddit: An Overview of Academic Research

TL;DR: In a recent survey as discussed by the authors, the main research directions that arose in recent years and focus primarily on the most popular platform, Reddit, are mapped and categorized according to their focus on the posts or on the users and different types of methodologies to extract information from the structure and dynamics of the system.
Journal ArticleDOI

Generative models of online discussion threads: state of the art and research challenges

TL;DR: Current generative models of the structure and growth of discussion threads are described, which are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms.
References
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Book

Diffusion of Innovations

TL;DR: A history of diffusion research can be found in this paper, where the authors present a glossary of developments in the field of Diffusion research and discuss the consequences of these developments.
Journal ArticleDOI

Diffusion of Innovations

Proceedings ArticleDOI

What is Twitter, a social network or a news media?

TL;DR: In this paper, the authors have crawled the entire Twittersphere and found 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.
Journal ArticleDOI

Twitter mood predicts the stock market.

TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
Proceedings Article

Measuring User Influence in Twitter: The Million Follower Fallacy

TL;DR: 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.