scholarly journals Indirect Causal Influence of a Single Bot on Opinion Dynamics Through a Simple Recommendation Algorithm

2022 ◽  
pp. 28-41
Author(s):  
Niccolo Pescetelli ◽  
Daniel Barkoczi ◽  
Manuel Cebrian
Keyword(s):  
Opinion Dynamics ◽  
Recommendation Algorithm ◽  
Causal Influence

scholarly journals Indirect causal influence of a single bot on opinion dynamics through a simple recommendation algorithm

2021 ◽  
Author(s):  
Niccolo Pescetelli ◽  
Daniel Barkoczi
Keyword(s):  
Direct Interaction ◽  
Opinion Dynamics ◽  
Dynamics Simulation ◽  
Recommendation Algorithm ◽  
Internal Representations ◽  
Agent Based ◽  
Causal Pathways ◽  
Average Population ◽  
Content Recommendation ◽  
Almost All

The ability of social and political bots to influence public opinion is often difficult to estimate. Recent studies found that hyper-partisan accounts often directly interact with already highly polarised users on Twitter and are unlikely to influence the general population's average opinion. In this study, we suggest that social bots, trolls and zealots may affect people’s views not just via a direct interaction (e.g. retweets, at-mentions and likes) and via indirect causal pathways through infiltrating platforms’ content recommendation systems. Using a simple agent-based opinion-dynamics simulation, we isolate the effect of a single bot – representing only 1% of the population – on the average opinion of Bayesian agents when we remove all direct connections between the bot and human agents. We compare this experimental condition with an identical baseline condition where such a bot is absent. We used the same random seed in both simulations so that all other conditions remained identical. Results show that, even in the absence of direct connections, the presence of the bot is sufficient to shift the average population opinion. Furthermore, we observe that the presence of the bot significantly affects the opinion of almost all agents in the population. Overall, these findings indicate that social bots and hyperpartisan accounts can influence average population opinions by changing platforms’ recommendation engines’ internal representations.

Evaluating the Creation and Interpretation of Causal Influence Models

2009 ◽  
Author(s):  
Dapeng Cao ◽  
Theresa K. Guarrera ◽  
Michael Jenkins ◽  
Priyadarshini R. Pennathur ◽  
Ann M. Bisantz ◽  
...  
Keyword(s):  
Causal Influence ◽  
The Creation

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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