Open AccessPosted Content
Information-Theoretic Measures of Influence Based on Content Dynamics
Greg Ver Steeg,Aram Galstyan +1 more
TL;DR: In this paper, the authors introduce content transfer, an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user's content on another's in a model-free way.
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Abstract: The fundamental building block of social influence is for one person to elicit a response in another. Researchers measuring a "response" in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most content on social networks is difficult to model because the modes and motivation of human expression are diverse and incompletely understood. We introduce content transfer, an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user's content on another's in a model-free way. Estimating this measure is made possible by combining recent advances in non-parametric entropy estimation with increasingly sophisticated tools for content representation. We demonstrate on Twitter data collected for thousands of users that content transfer is able to capture non-trivial, predictive relationships even for pairs of users not linked in the follower or mention graph. We suggest that this measure makes large quantities of previously under-utilized social media content accessible to rigorous statistical causal analysis.
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Citations
Inferring pairwise influence from encrypted communication
Brian Thompson,Hasan Cam +1 more
- 17 Dec 2015
TL;DR: This work presents an efficient algorithm to infer influence between entities that relies only on the times of their individual activity, paying particular attention to the computational challenges posed by large, high-volume networks.
3
Personalized PageRank Based Feature Selection for High-dimension Data
Zhibo Zhu,Qinke Peng,Xinyu Guan +2 more
- 01 Oct 2019
TL;DR: A feature selection method based on the personalized PageRank which can provide a better measure of the high-order relationship between the candidate feature and the subset of selected features and outperforms popular benchmarks.
2
Selecting transfer entropy thresholds for influence network prediction
Dave McKenney,Tony White +1 more
TL;DR: This work builds upon the existing work by using transfer entropy to predict a graph, in which links represent influence between two agents, and shows that this method generally allows the influence network to be predicted with precision and recall values of over 90% across three common theoretical network classes.
2
A network perspective on intermedia agenda-setting
TL;DR: In this paper, a large-scale, data-driven investigation is conducted to quantify the impact of intermedia agenda-setting in specific countries or contexts, and they find that the influence networks associated with most topics exhibit small world properties, which play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network.
•Posted Content
A Variational Topological Neural Model for Cascade-based Diffusion in Networks.
TL;DR: A topological recurrent neural model is proposed, which embeds the history of diffusion in infected nodes as hidden continuous states and exhibits good experimental performances for diffusion modelling and prediction.
2
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