Identifying exogenous and endogenous activity in social media

Fujita, Kazuki; Medvedev, Alexey; Koyama, Shinsuke; Lambiotte, Renaud; Shinomoto, Shigeru

doi:10.1103/physreve.98.052304

Cited by 20 publications

(17 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Endogenous factors are defined as interactions within social media or social networks, while exogenous factors are external influences on a community, such as news, catastrophes, and social events, which typically happen externally to social media. Previous studies have suggested that these factors can affect the shape of peaks in popularity profiles such as in search queries on Google, viewing activity on YouTube [13], and the adoption of hashtags on Twitter [19,44]. We develop a predictive model incorporating topic trends as exogenous factors and a method for estimating these factors from observational social media data.…”

Section: User Modeling In Social Mediamentioning

confidence: 99%

Estimating community feedback effect on topic choice in social media with predictive modeling

et al. 2020

View full text Add to dashboard Cite

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback-called community feedback-to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author's decision to continue the topic of their previous post. We explore the confounding factors, including author's topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33% and 14% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14%, depending on the user and the amount of feedback.

show abstract

Section: User Modeling In Social Mediamentioning

confidence: 99%

Estimating community feedback effect on topic choice in social media with predictive modeling

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Various point process models have been proposed for predicting the final number of re-shares [19,21] and their temporal pattern [10] on social media. Furthermore, these models have been applied to interpret the endogenous and exogenous shocks to the activity on YouTube [22] and Twitter [23]. To the best of our knowledge, the proposed model is the first model incorporating a two-stage process that is an essential characteristic of the spread of fake news.…”

Section: Related Workmentioning

confidence: 99%

Modeling the spread of fake news on Twitter

et al. 2021

View full text Add to dashboard Cite

Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.

show abstract

“…The endogenous factors mainly refer to intrinsic self-excitation mechanisms, while the exogenous factors come from environmental changes or other events that take place independently of events that have occurred in a system. Typical instances could be found in seismic dynamics [1][2][3][4], tweets in social networks [5], neuronal firing [6,7], and so on. In this respect, financial markets are the most representative.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mutual Exciting Structure in Stock Price Trend Dynamics

Jiang

et al. 2021

Entropy

View full text Add to dashboard Cite

We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading.

show abstract

Identifying exogenous and endogenous activity in social media

Cited by 20 publications

References 47 publications

Estimating community feedback effect on topic choice in social media with predictive modeling

Estimating community feedback effect on topic choice in social media with predictive modeling

Modeling the spread of fake news on Twitter

Detection of Mutual Exciting Structure in Stock Price Trend Dynamics

Contact Info

Product

Resources

About