Inducing Personalities and Values from Language Use in Social Network Communities

Kumar, Uttam; Reganti, Aishwarya N.; Maheshwari, Tushar; Chakroborty, Tanmoy; Gambäck, Björn; Das, Amitava

doi:10.1007/s10796-017-9793-8

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…This position is supported by many studies on the topic [19,[25][26][27][28]. In [29], Kumar et al extracted the personality traits from written text. They collected a huge text corpus from Facebook, Twitter, and essays correlating the social network structure with personality assessment questionnaires.…”

Section: Lexical Hypothesis and Nlp In Personality Estimationmentioning

confidence: 83%

Multilingual Transformer-Based Personality Traits Estimation

et al. 2020

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Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media platforms, numerous studies dealt with measuring personality traits by gathering users’ information from their social media profiles. Real world applications showed how natural language processing combined with supervised machine learning algorithms are effective in this field. These applications have some limitations such as focusing on English text only and not considering polysemy in text. In this paper, we propose a multilingual model that handles polysemy by analyzing sentences as a semantic ensemble of interconnected words. The proposed approach processes Facebook posts from the myPersonality dataset and it turns them into a high-dimensional array of features, which are then exploited by a deep neural network architecture based on transformer to perform regression. We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions. We obtained state-of-the-art results in personality traits estimation from social media posts for all five personality traits.

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Section: Lexical Hypothesis and Nlp In Personality Estimationmentioning

confidence: 83%

Multilingual Transformer-Based Personality Traits Estimation

et al. 2020

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“…The data was collected using the crowdsourcing services Amazon Mechanical Turk (AMT) 3 and Rapid Workers 4 [54]. Nearly 600 users participated in the data collection process, and each of them was asked to select at least 10 followers and 10 followings along with their relationship (friends, relatives, colleagues, other).…”

Section: Data: the Relations Between Usersmentioning

confidence: 99%

Understanding the Psycho-Sociological Facets of Homophily in Social Network Communities

Solomon

Pykl

Das

et al. 2019

IEEE Comput. Intell. Mag.

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Community" in social networks is a nebulous concept. A community is generally assumed to be formed by people who possess similar attributes or characteristics, also known as "homophily". Although there has been a lot of research on community detection based on network topology, the semantic interpretation of communities is rarely studied. The present work aims to understand the behavioural similarity of users present in their personal neighbourhood communities formed by friends, relatives, or colleagues, and addresses two fundamental questions: (i) Are communities formed by users who possess similar behavioural traits? If so, does this apply to all those sub-networks, i.e., friends, relatives, and colleagues? (ii) Does adding behavioural node-specific attributes/features to the nodes in a network lead to better community detection? To better understand the psycho-sociological homophilic nature of personal networks, the personalities and values of Twitter users were analysed using the well-established "Big-5 personality model" and "Schwartz sociological behaviour model". Empirical results based on the psycho-sociological behaviour show that friends networks exhibit homophily, whereas relatives and colleagues networks do not exhibit such homophilic behaviour. It can also be observed that neurotic people tend to behave heterogeneously with people of various personality traits. In addition, it is shown that such empirical evidence can be used as features for the tasks of community detection and link prediction.

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“…We analyze how an individuals grit correlates with their personality using the Big Five personality traits [7]. We use the personality classifier from [19], with a reported F-score of 81%. Hence, we obtain average regression values for each personality trait corresponding to each grit class.…”

Section: ) Grit and Personalitymentioning

confidence: 99%

The Nitty–GRITties of Success: Computational Analysis of Grit From Language

Maheshwari¹,

Reganti

Poria

et al. 2019

IEEE Access

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Grit is a prominent personality trait that measures an individuals passion and perseverance for long-term goals. Grit entails that zeal and persistence of motive play a key role in determining an individuals success in the long run, as opposed to natural talent. But how does one identify and distinguish between gritty and non-gritty individuals? Do they use language differently? In this paper, we seek to answer these questions using a social media setting. We build a new crowd-sourced Twitter corpus that contains the posts of 464 users along with their grit scores, and explore how grit correlates with other major behavioural and personality traits. We then train machine learning classification models to predict the grit level of an individual using his/her Twitter posts, and show that language can be effectively used to infer this personality trait. INDEX TERMS Computational linguistics, computational social sciences, natural language processing.

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Inducing Personalities and Values from Language Use in Social Network Communities

Cited by 11 publications

References 24 publications

Multilingual Transformer-Based Personality Traits Estimation

Multilingual Transformer-Based Personality Traits Estimation

Understanding the Psycho-Sociological Facets of Homophily in Social Network Communities

The Nitty–GRITties of Success: Computational Analysis of Grit From Language

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