Advances in Machine Learning for the Behavioral Sciences

Kliegr, Tomáš; Bahník, Štěpán; Fürnkranz, Johannes

doi:10.1177/0002764219859639

Cited by 14 publications

(8 citation statements)

References 113 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, as our object of study focuses on the field of political communication, choosing Twitter is justified as the space par excellence used by political players and journalists to communicate on, talk about, and discuss politics in the social media environment, due to the high presence of these players on this platform (Casero-Ripollés, 2018). The methodology is based on machine learning applied to the digital behaviour of Twitter users (Kliegr, Bahník, & Fürnkranz, 2020). Conversations on Twitter about the negotiations on the formation of the government in Spain are identified in an automated fashion.…”

Section: Methodsmentioning

confidence: 99%

Digital Public Sphere and Geography: The Influence of Physical Location on Twitter’s Political Conversation

Casero-Ripollés

Micó

Bosch

2020

MaC

View full text Add to dashboard Cite

Social media has instituted new parameters for the political conversation in the digital public sphere. Previous research had identified several of these new phenomena: political polarisation, hate speech discourses, and fake news, among others. However, little attention has been paid to the users’ geographical location, specifically to the role location plays in political discussion on social media, and to its further implications in the digital public sphere. A priori, we might think that on the digital landscape geographical restrictions no longer condition political debate, allowing increasingly diverse users to participate in, and influence, the discussion. To analyse this, machine learning techniques were used to study Twitter’s political conversation about the negotiation process for the formation of the government in Spain that took place between 2015 and 2016. A big data sample of 127,3 million tweets associated with three Spanish cities (Madrid, Barcelona, and Valencia) was used. The results show that the geographical location of the users directly affects the political conversation on Twitter, despite the dissolution of the physical restrictions that the online environment favours. Demographics, cultural factors, and proximity to the centres of political power are factors conditioning the structure of digital political debate. These findings are a novel contribution to the design of more effective political campaigns and strategies, and provide a better understanding of the dynamics of the digital public sphere provided by Twitter.

show abstract

Section: Methodsmentioning

confidence: 99%

Digital Public Sphere and Geography: The Influence of Physical Location on Twitter’s Political Conversation

Casero-Ripollés

Micó

Bosch

2020

MaC

View full text Add to dashboard Cite

show abstract

“…The signs of art therapy center serve actors in design form. When expressing the visual effect, the process of constructing the signage design in art therapy centers should take into full consideration [ 36 ]. The visual effect should be taken into account the psychological and physiological characteristics of the audience, and create psychological harmony for patients as much as possible, so as to surpass physical harmony.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Wang

Zhang

2022

Occupational Therapy International

View full text Add to dashboard Cite

With the development of society, psychological health becomes a basic standard for a college student to grow into a qualified person. This study is aimed at using data mining principles and methods to excavate the factors that lead to psychological problems of college students, to purposefully carry out psychological interventions, use visual art design methods to promote college students’ psychological health treatment, and build a perfect system of college students’ psychological treatment. Based on the theories of data mining, we built a data analysis model, elaborated the data preprocessing method, and applied the Apriori algorithm to analyze the data of obsessive-compulsive symptoms and interpersonal sensitivity symptoms, and various psychological problem attributes extracted the strong association rules and analyzed the results. Take advantage of the corresponding unique school environment and educational advantages, to build a set of mental health education methods suitable for modern college students, and truly enable them to obtain satisfactory psychological interventions in a reasonable art design treatment phase. Based on the above association pattern mining results, a series of preprocessing operations were performed on the data, and then, the Apriori algorithm was applied to discover the potential association relationships among 9 psychological dimension factors of college students, and then, the ID3 decision tree algorithm was used to construct a decision tree and pruning process, from which the classification rules of students’ psychological problems were analyzed and discovered. These studies provide some practical reference basis for school counseling work.

show abstract

“…These models are usually described as "black-box" models because the models they make cannot be readily understood. For policymakers, such as a banker or an economist, they are more willing to resort to the model that can be easier to remember and apply, and also that can be easily explained to the public [180]. Accordingly, the idea of interpreting black box models has drawn more attention in recent years.…”

Section: Interpretable Machine Learningmentioning

confidence: 99%

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Zhang

Valencia

Chang

2023

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

show abstract

Advances in Machine Learning for the Behavioral Sciences

Cited by 14 publications

References 113 publications

Digital Public Sphere and Geography: The Influence of Physical Location on Twitter’s Political Conversation

Digital Public Sphere and Geography: The Influence of Physical Location on Twitter’s Political Conversation

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Contact Info

Product

Resources

About