Temperament detection based on Twitter data: classical machine learning versus deep learning

Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007

Emoji Use in Social Media Posts: Relationships with Personality Traits and Word Usage

Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples (n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants’ consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use of you pronouns, I pronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users’ characteristics may be gleaned from the content of their social media posts

WHO’S FOLLOWING YOU? CYBER VIOLENCE ON SOCIAL MEDIA

Social media use has become an integral part of daily life. Within these increasingly influential online communities, a proportion of users are subject to negative online contact in a phenomenon labelled cyberviolence. Cyberviolence is defined as harm delivered by electronic means to a person or people who perceive this contact as negative.A review of existing literature revealed that, despite reliance on distinct offline definitions, all behaviours explored could be classified according to three key themes: sexual, threatening and humiliating cyberviolence. To assess the prevalence of these forms of cyberviolence across social media, 370 participants completed an online survey that featured items relating to victimisation and perpetration, as well as a number of well-established personality measures. These measures explored key traits and models of personality including the Big Five model to assess the potential role of an individual’s personality in their engagement in cyberviolence. The results of this thesis suggest that differences exist between those involved in cyberviolence and those who do not engage in cyberviolence on certain key personality traits including psychopathy and narcissism. Models of cybervictimisation, perpetration and a hybrid of cybervictimisation/perpetration revealed that these traits explained approximately ten percent of the variance in cyberviolence indicating that other factors, besides individual personalities, may have more influence over engagement in and/or experience of these behaviours. Overall findings suggest that there is little to demarcate those involved in cyberviolence, as victims or perpetrators, leading to the conclusion that this is not a niche area of deviance, but may be a mainstream side effect of social media use. The implications of these findings are discussed

Personality Recognition on Social Media With Label Distribution Learning

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual&#39;s personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users&#39; profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.</p