I Alone Can Fix It: Examining interactions between narcissistic leaders and anxious followers on Twitter using a machine learning approach

Abstract

Due to their confidence and dominance, narcissistic leaders oftentimes can be perceived favorably by followers, in particular during times of uncertainty. In this study, we propose and examine the relationship between narcissistic leaders and followers who are prone to experience uncertainty intensely and frequently in general, namely highly anxious followers. We do so by applying machine learning algorithms to account for personality traits in a large sample of leaders and followers on Twitter. We find that highly anxious followers are more likely to interact with narcissistic leaders in general, and male narcissistic leaders in particular. Finally, we also examined these interactions in the context of highly popular leaders and found that as leaders become more popular, they begin to attract less anxious followers, regardless of leader gender. We interpret and discuss these findings in relation to previous work and outline limitations and future research recommendations based on our approach.

Don’t you tweet me badly: Anxiety contagion between leaders and followers in computer-mediated communication during COVID-19

“…Firstly, although our applied ML methodology allows us to score millions of tweets across a long period of time, it could be argued that social media profiles might not reflect actual personality but rather an idealized form of user representation. Yet, previous research has found strong support for assessing personality traits in social media posts [38,39]. Indeed, we would argue that the anxiety detection algorithm specifically is superior to self-report ratings of anxiety because the onset of anxiety is not always immediately evident to the experiencing person and lead to an increased likelihood of burnout and exhaustion over time if not recognized.…”

“…Hence, we measure and control for all Big Five leader and follower personality traits. Previous work [e.g., [36][37][38][39] has shown that personality traits can be measured accurately and successfully in online contexts using social media data. Therefore, in our third step, each Twitter profile was fed into the IBM Watson Personality Insights PLOS ONE API, which extracts and analyzes social media textual data to identify personality traits based on linguistic analysis [40].…”

Tweet you right back: Follower anxiety predicts leader anxiety in social media interactions during the SARS-CoV-2 pandemic

“…We also included the Big Five personality traits as controls in our analysis. Previous work [e.g., [32][33][34][35] has demonstrated that social media data provides a continuous stream of data that can be used to accurately and reliably measure personality traits. The Big Five personality traits for all individuals in our sample were obtained using the IBM Watson Personality Insights API [36], an open-vocabulary machine-learning approach trained using a reference sample of 1,000,000 individuals.…”

All about that trait: Examining extraversion and state anxiety during the SARS-CoV-2 pandemic using a machine learning approach

“…Linguistic-based text analytics or linguistic analytics exploits information about the syntax and semantics of a language as well as lexicons, to extract important information from textual data. For instance, while Gruda et al (2020) , Gruda, Karanatsiou, Mendhekar, Golbeck, & Vakali, (2021) , Gruda, Karanatsiou, Hanges, Goldbeck, & Vakali, (2021) , and Karanatsiou et al (2020) applied linguistic-based text analytics on Twitter data to detect traits such as narcissism and attachment, Ojo and Rizun (2019) employed linguistic markers found in negative free-text comments provided by hospital service users to determine the frequency and intensity of the associated negative experience. In addition, more recent work by Gruda and Ojo (2021) and Gruda, Ojo & Psychogios (2021) have also demonstrated that mental health signals can be identified from publicly available Twitter data.…”