Multi-view Personality Profiling Based on Longitudinal Data

Buraya, Kseniya; Farseev, Aleksandr; Filchenkov, Andrey

doi:10.1007/978-3-319-98932-7_2

Cited by 16 publications

(14 citation statements)

References 21 publications

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“…The sphere that generates the largest interest from the international researchers is Russian politics, and this is reflected in the dominant position of this topic among Russian SNS-based studies. Sociological research is somewhat fragmented, and psychology studies are least of all related to Russia, with some strong studies done by the Russian scholars not using Russian data at all (Buraya et al 2018).…”

Section: Resultsmentioning

confidence: 99%

Social Media-based Research of Interpersonal and Group Communication in Russia

Koltsova

Porshnev

Sinyavskaya

2020

The Palgrave Handbook of Digital Russia Studies

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Rapidly proliferating social media not only serve as a new channel of human communication but also open up research opportunities to ask a wider set of questions about political, sociological and psychological factors that influence interpersonal and group online communication, development and maintenance of personal networks and the growth or decline of social capital. In this chapter we discuss the research opportunities provided by new survey, observational and experimental data that may be obtained from a social networking site. For doing so, we refer to Russian-language social networking sites (SNS) or SNS segments, notably VKontakte as the most popular SNS in Russia. We demonstrate how the aforementioned types of data may or have already been used to address research tasks from a number of disciplines.

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Section: Resultsmentioning

confidence: 99%

Social Media-based Research of Interpersonal and Group Communication in Russia

Koltsova

Porshnev

Sinyavskaya

2020

The Palgrave Handbook of Digital Russia Studies

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“…Regarding the use of visual information from posts shared in social networks to address mental disorders, the closest approaches to our goal are methods for personality prediction [ 19 ], and a few recent approaches address self-harm, depression, and anxiety [ 20 - 22 ]. We believe that our study is the first image-based approach for suicide risk assessment on social media at the user level.…”

Section: Introductionmentioning

confidence: 99%

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis

Ramírez‐Cifuentes¹,

Freire²,

Baeza-Yates³

et al. 2020

J Med Internet Res

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Background Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.

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“…However, automatic personality inference is also known to be a hard task [5,21], which is mainly due to the multi-facet nature of social media data. For example, Twitter is often used for casual daily interactions, while Facebook nowadays more perceived as a private communication channel.…”

Section: Introductionmentioning

confidence: 99%

“…by the research community[5,6] and splits one's personality into 16 types, each formed by the following four binary dimensions:• Extroversion and Introversion (EI): this dimension determines how an individual focuses her energies and interest, whether she is influenced externally by the opinion and interpretation of others (Extroverts) or motivated by her inner thoughts (Introverts). • Sensing and iNtuition (SN): this aspect demonstrates how people interpret knowledge.…”

mentioning

confidence: 99%

Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

Yang

Farseev

Filchenkov

2021

Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval

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Human personality traits are the key drivers behind our decisionmaking, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction. CCS CONCEPTS• Computing methodologies → Machine learning; • Applied computing → Psychology.

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Multi-view Personality Profiling Based on Longitudinal Data

Cited by 16 publications

References 21 publications

Social Media-based Research of Interpersonal and Group Communication in Russia

Social Media-based Research of Interpersonal and Group Communication in Russia

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis

Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

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