scholarly journals How social learning amplifies moral outrage expression in online social networks

2021 ◽  
Vol 7 (33) ◽  
pp. eabe5641
Author(s):  
William J. Brady ◽  
Killian McLoughlin ◽  
Tuan N. Doan ◽  
Molly J. Crockett
Keyword(s):  
Social Networks ◽  
Reinforcement Learning ◽  
Social Learning ◽  
Online Social Networks ◽  
Social Life ◽  
Moral Outrage ◽  
Social Feedback ◽  
Learning Mechanisms ◽  
Behavioral Experiments ◽  
Social Learning Processes

Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces.

scholarly journals How social learning amplifies moral outrage expression in online social networks

2021 ◽  
Author(s):  
William J. Brady ◽  
Killian Lorcan McLoughlin ◽  
Tuan Nguyen Doan ◽  
Molly Crockett
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Online Social Networks ◽  
Social Life ◽  
Learning Processes ◽  
Moral Outrage ◽  
Social Feedback ◽  
Learning Mechanisms ◽  
Behavioral Experiments ◽  
Social Learning Processes

Moral outrage shapes fundamental aspects of human social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two pre-registered observational studies of Twitter (7,331 users and 12.7 million total tweets) and two pre-registered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. We also find that outrage expressions are sensitive to expressive norms in users’ social networks, over and above users’ own preferences, suggesting that norm learning processes guide online outrage expressions. Moreover, expressive norms moderate social reinforcement of outrage: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to impact moral discourse in digital public spaces.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

scholarly journals How do humans learn about other people? Incorporating social knowledge structures into reinforcement learning

2021 ◽  
Author(s):  
Koen Frolichs ◽  
Gabriela Rosenblau ◽  
Christoph Korn
Keyword(s):  
Reinforcement Learning ◽  
Social Learning ◽  
Personality Traits ◽  
Reference Points ◽  
Knowledge Structures ◽  
Social Knowledge ◽  
Levels Of Detail ◽  
The People ◽  
Behavioural Experiments ◽  
Social Learning Processes

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture such complex social learning processes are currently lacking. In this study, we specified and tested potential strategies that humans could employ for learning about others. Standard reinforcement learning (RL) models only capture part of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalized two social knowledge structures and implemented them in novel hybrid RL models to test their usefulness across different social learning tasks. We named these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results indicated that participants combined the concepts of granularity and reference points in a rather optimal fashion—with the specific optimal combinations in models depending on the people and trait items that participants learned about. Overall, our experiments demonstrate that variants of RL algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

scholarly journals A ML and NLP based Framework for Sentiment Analysis on Bigdata

2020 ◽  
Vol 8 (5) ◽  
pp. 189-200
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Online Social Networks ◽  
Topic Model ◽  
Social Feedback ◽  
Feedback Systems ◽  
Multiple Sources ◽  
Probabilistic Topic Model ◽  
Novel Approach

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.

scholarly journals Same wavelength group identification from online social networks: A general framework

2014 ◽  
Vol 11 (1) ◽  
pp. 229-239
Author(s):  
Rafeeque Chalil ◽  
Selvaraju Sendhilkumar
Keyword(s):  
Social Networks ◽  
Polynomial Time ◽  
Online Social Networks ◽  
Social Life ◽  
General Framework ◽  
Social Issues ◽  
Group Identification ◽  
Response Patterns ◽  
Social Scientists ◽  
Small Set

Reacting to social issues or events through Online Social Networks has become a social habit. Social scientists have identified several network relationships and dimensions that induce homophily. Sentiments or opinions towards different issues have been observed as a key dimension which characterizes human behaviour. People usually express their sentiments towards various issues. Different persons from different walks of social life may share same opinion towards various issues. When these persons constitute a group, such groups can be conveniently termed same wavelength groups. We propose a novel framework based on sentiments and an algorithm to identify such same wavelength groups from online social networks like twitter. The proposed algorithm generates same wavelength groups in polynomial time for relatively small set of events. The analysis of such groups would be of help in unravelling their response patterns and behavioural features.

scholarly journals When protests turn violent: The roles of moralization and moral convergence

2017 ◽  
Author(s):  
Marlon Mooijman ◽  
Joseph Hoover ◽  
Ying Lin ◽  
Heng Ji ◽  
Morteza Dehghani
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Behavioral Experiments ◽  
Moral Rhetoric ◽  
The Future ◽  
Using Data

We propose that the risk of violence at protests can be estimated as a function of individual moralization and perceived moral convergence. Using data from the 2015 Baltimore protests, we find that not only did the rate of moral rhetoric on social media increase on days with violent protests, but also that the hourly frequency of morally relevant tweets predicted the future rates of arrest during protests, suggesting an association between moralization and protest violence. To understand the structure of this association, we ran a series of controlled behavioral experiments demonstrating that people are more likely to endorse violent protest for a given issue when they moralize the issue; however, this effect is moderated by the degree people believe others share their values. We discuss how online social networks may contribute to inflations of protest violence.

scholarly journals A Comparison of Online Social Networks and Real-Life Social Networks: A Study of Sina Microblogging

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Dayong Zhang ◽  
Guang Guo
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Interpersonal Relationships ◽  
Online Social Networks ◽  
Social Life ◽  
Low Cost ◽  
Online Social Network ◽  
Real Life ◽  
Human Communication ◽  
Cognitive Constraints

Online social networks appear to enrich our social life, which raises the question whether they remove cognitive constraints on human communication and improve human social capabilities. In this paper, we analyze the users' following and followed relationships based on the data of Sina Microblogging and reveal several structural properties of Sina Microblogging. Compared with real-life social networks, our results confirm some similar features. However, Sina Microblogging also shows its own specialties, such as hierarchical structure and degree disassortativity, which all mark a deviation from real-life social networks. The low cost of the online network forms a broader perspective, and the one-way link relationships make it easy to spread information, but the online social network does not make too much difference in the creation of strong interpersonal relationships. Finally, we describe the mechanisms for the formation of these characteristics and discuss the implications of these structural properties for the real-life social networks.

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