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Negativity and anti-social attention seeking among narcissists on Twitter: A linguistic analysis
Abstract
A linguistic analysis shows differences in the way narcissistic and non-narcissistic users communicate on Twitter. Because narcissism is marked by attention-seeking, and is related to negativity and perceived victimization, we hypothesized that narcissists would use more words about anger and negative emotions. Conversely, we further hypothesized that they would use fewer words about social interaction and positive emotions. An analysis of over 1,000 users supported these hypotheses.
... Moreover, narcissist individuals seem to experience increased hostility from comparison [17] and show elevated aggression in reaction to ego-threatening feedback [16]. Such a state of mind may manifest in linguistic patterns like an elevated use of first person singular pronouns [14], a more frequent use of sexual words [18], or a rare use of words related to affiliation and social connection [19]. Furthermore, narcissistic individuals use more antisocial, swear and anger words [19], [18], and less positive emotion words. ...
... Such a state of mind may manifest in linguistic patterns like an elevated use of first person singular pronouns [14], a more frequent use of sexual words [18], or a rare use of words related to affiliation and social connection [19]. Furthermore, narcissistic individuals use more antisocial, swear and anger words [19], [18], and less positive emotion words. Interestingly, there are no disparities between individuals with high versus low narcissism in the use of anxiety-related and sad words [19]. ...
... Furthermore, narcissistic individuals use more antisocial, swear and anger words [19], [18], and less positive emotion words. Interestingly, there are no disparities between individuals with high versus low narcissism in the use of anxiety-related and sad words [19]. ...
Language analyses reveals crucial information about an individual’s current state of mind. Maladaptive psychological functioning appears in cognition, emotional experience and behaviour. In the time of the internet of things, a vast number of text and speech is available; subsequently, the interest in the automated detection of psychological functioning via language is rising. The current study indicates that depression and narcissism can be predicted through word use in personal narratives. Both conditions are characterised by an altered word count regarding anxiety and we (LIWC-based). While depressive individuals use less social words and more anxietyrelated words, narcissists do the opposite. This might reflect the verbal correlate of the cognitive triad in depression. In contrast, narcissists’ word use mirrors their excommunicated anxiety of being an undesired self and their inability to reach long-term goals due to a lack of impulse control. The automated recognition of mental state through word use could improve early detection of mental disease, monitoring of disease course, delivery of tailored interventions and evaluation of therapy
outcome.
... According to Fan et al. [41], not overt narcissism but covert narcissism significantly predicts cyberbullying perpetration and victimization. On the other hand, Golbeck [47] found that individuals with higher overt narcissism use curse words, negative emotion words, anger words, and anti-social words than lowers in Twitter. It is also found that people high in overt narcissism are sending more tweets about themselves [93]. ...
With more and more people engaging in virtual environments, the virtual goods market is highly profitable and expanding rapidly. Literature to date suggests that people buy in-game content with many different motivations (social, functional, perceived enjoyment, etc.), but studies have remained silent on how narcissism affects in-game buying behavior and motivations. This study aimed to delineate the role of overt (grandiose) narcissism and vulnerable (covert) narcissism on virtual goods purchase intention and motivations. An online quantitative survey collected data from online gamers (N = 401). Participants were invited via online playing and streaming platform Twitch.tv, and Discord gaming channels. Results demonstrate that overt narcissism but not covert narcissism predicts in-game purchase intention. The perceived social visibility of virtual goods was found as a mediator of the positive relationship between overt narcissism and purchase intention. By analyzing the inventory of in-game purchase motivations, four distinct motivations were explored: unobstructed play, in-game symbolic consumption, economic rationale, and social interaction. The findings suggest that both types of narcissism are positively related to motivations for unobstructed play and in-game symbolic consumption. Additionally, covert narcissism was positively related to economic rationale motivation. Findings are discussed both for practitioners and researchers.
... Bu davranış genellikle benliklerini güçlendirme etkisine sahiptir, halk tarafından saygı duyulmak ve popülerliklerini artırmak, aynı zamanda başkalarının dikkatini çekmek ve tanınmak için gerçek kişiliklerini maskeleyerek kendilerini pazarlamak için çaresiz olan insanları tasvir eden anarşist bir toplum yaratır (Chua ve Chang, 2016). Bununla birlikte, daha üzücü bir şekilde, aynı zamanda, kaygı ve depresyon gibi diğer psikolojik sonuçların yanı sıra sağlıksız kıskançlık seviyelerine yol açan bireyler arasında sürekli bir karşılaştırma ve rekabet dalgasına neden olur (Vilcoks ve Stephan, 2012;Chua ve Chang, 2016;Golbeck, 2016). ...
... The Twitter platform offers live and real-time communication among users who share their thoughts on issues and events, and plays an important role to connect people (Golbeck, 2016). Twitter conversations take place seamlessly because each tweet can be followed easily, and the interactions are restricted to the user's network of friends or followers (Honeycutt & Herring, 2009). ...
Social media communication has its own language features and one aspect is modified spelling of standard words. Social media users use shortened words with full awareness of the meanings, and new non-standard words are constantly added to the repertoire of social media language. A pertinent question is whether social media users learn these non-standard words to use or whether they also contribute to the vocabulary used in social media communication. The study examined Malaysian millennials' use of non-standard words in Twitter and their reasons for shortening words. For the non-standard words, data were collected from 200 active Twitter users whereas data on reasons for shortening words were collected from 30 users. The results showed that the Malaysian millennials frequently used non-standard spelling of words. The three top words were "ni" (this), "nak" (want), and X (negation). The main reasons for the Twitter users to shorten words were the 280-character limit per tweet, user convenience, and characteristics of words. The Twitter users felt free to create new spellings of standard words at times for fun, but most of the time, they use the common non-standard words. The Malaysian millennials reported that they were inclined to shorten long and complex words, and words with many vowels. The study suggests that Twitter users balance between speed in communication and preservation of meaning when using non-standard words.
... In both Chennai and Sydney metro rail projects, we also noticed several tweets that represented some negative perception of the project. This echoed the literature that people are often more vocal about criticism than praise (Park, 2015;Golbeck, 2016). ...
Infrastructure projects such as metro rails are being increasingly built in busy cities mainly to improve mobility and reduce congestion. However, assessment of benefits realized from these projects is complex. One reason for this is that promoters of these projects often misrepresent the projects’ benefits to get them approved. Although some benefits from infrastructure projects can be measured using economic data, such data are insufficient for measuring social benefits. This article reports on an exploratory study on how social media could provide an opportunity to evaluate benefits qualitatively by analyzing tweets from metro rail projects in India and Australia. Although the analysis of tweets from these projects indicated that citizens who use these transport facilities report benefits, they do not seem to use the same terms as the project’s promoters to describe these benefits. The article concludes with some suggestions on how social media can supplement current methods used in evaluating benefits from transport projects.
... Emotional (affective) language, for example, signals important aspects of the speaker, including sex and status. Women more than men use language with positive emotion, 1,3 as do those high in extraversion, 4 conscientiousness, 5 and agreeableness. 6,7 Not surprisingly, celebrities using Twitter most often discuss their preferences, 8 yet affect language among celebrities on Twitter also differs, with positive emotion seen in less "followed" (i.e., lower social status) celebrities, regardless of sex, 9 perhaps because those persons wanted to be seen as agreeable, and light-therefore, more fun to follow. ...
... Research reveals that narcissists use more profane and antisocial terms. For example, a linguistic analysis of 1000 Twitter accounts revealed that people with higher levels of narcissism were found to use more words about anger and negative emotions and fewer words about social interaction (Golbeck, 2016). Indeed, Marshall, Lefringhausen, and Ferenczi (2015) in a study of over 550 Facebook users report that narcissists' used Facebook for attention-seeking and validation of their views. ...
... 12 Moody, over-reactive, and selfindulgent people (e.g., those high in neuroticism) use negative emotional words, 14,19 but negative language is less likely among persons who are high in conscientiousness. 7 In many contexts, particularly those surrounding anxiety-provoking situations, women express more negative emotion. 13 People who are unpredictable more commonly use words of anger to express themselves, 19 as domen, 18 although anger in linguistic expression is tolerated only among those of higher status. ...
The rise of social media platforms has changed human communication once and forever and consequently, has triggered an array of emotions such as fear of missing out (FoMO). In two different studies, this paper investigated individual differences, psychological and social media motivational variables as predictors of FoMO. The paper also sought to test whether FoMO will have a negative impact on academic performance above and beyond social media engagement and social media addiction. Using a structural equation modelling (SEM) along with multiple mediation analysis, the results indicated that perceived excessive social media content generation, attention seeking, and personality variables significantly predicted FoMO controlling for individual differences and social media activity such as Instagram followers, Twitter followers, as well as WhatsApp engagement level. Furthermore, the biological sex of the individual was found to moderate the relationship between attention seeking and FoMO. Findings also indicated that FoMO had a positive impact on both social media engagement and social media addiction. Parallel mediation analysis revealed that FoMO had a negative effect on academic performance above and beyond social media addiction.
The goal of the present study is to investigate the sociolinguistic aspects in relation to the existence of the evil eye as a belief system in Kuwaiti society. Specifically, we examine the verbal and nonverbal manifestations of the evil eye, even at a distance, when its effect is believed to permeate into online interaction on social media. To achieve this goal, we used a combined research method that consists of an online questionnaire that reached an extensive group of 518 participants and a face-to-face interview with a group from our sample in order to elicit both quantitative and qualitative data. The results demonstrate that believing in the evil eye and its harmful effects is prevalent among all groups of people in the Kuwaiti society, regardless of sociolinguistic factors, which affect the frequency and nature of posts. Our findings also indicate that the same verbal and nonverbal methods of protection from the evil eye used in the offline world are used online. Moreover, these preventative methods appear to be in support of similar ones mentioned in previous literature related to this area.
Bright ICT promises a new era of IT adoption and use, however, in this current era of ubiquitous computing and social media platforms, we have witnessed IS users being rendered powerless in the information systems development process and professionally manipulated by large technology companies through the algorithmic structures of social networking platforms. In 1987 Markus & Bjorn-Andersen warned us of the potential of consequences of this situation which are now evident on a daily basis where data privacy is compromised, social media platform content is difficult if not impossible to manage and our political and economic systems are disrupted. This paper outlines an engaged research approach being taken by the RISE_SMA project to develop more innovative theoretical approaches and methods for analysing social media data for the assurance of social cohesion during times of crisis.
Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality -- the dark triad -- consisting of narcissism, psychopathy and Machiavellianism, is related to observable Twitter behavior such as platform usage, posted text and profile image choice. Our results show that we can map various behaviors to psychological theory and study new aspects related to social media usage. Finally, we build a machine learning algorithm that predicts the dark triad of personality in out-of-sample users with reliable accuracy.
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