List of Titles

Catching the catfish : exploring gender and the dark tetrad of personality as predictors of catfishing perpetration

Authors: Lauder, Cassandra , March, Evita
Date: 2023
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 140, no. (2023), p.
Full Text: false
Reviewed:
Description: Catfishing, the act of deceiving and exploiting another person online, can have significant negative impact on the target. To date, limited research has explored individual differences in perpetration of catfishing. We address this paucity by adopting an evolutionary psychology theoretical framework (the “cheater strategy” hypothesis) and exploring the utility of gender and the “Dark Tetrad” personality traits of psychopathy, sadism, Machiavellianism, and narcissism to predict catfishing perpetration. A sample of 664 participants (55.8% men, 40.3% women) with an average age of 28.84 years (SD = 9.60) were recruited via social media and completed an anonymous online questionnaire which comprised measures of personality and catfishing behaviours. Combined, the variables explained 62.6% of variance in catfishing perpetration. Results partially supported the hypotheses, with only psychopathy, sadism, and narcissism emerging as positive predictors of catfishing perpetration. Findings of the current study indicate that evolutionary psychology may be a useful theoretical framework when exploring antisocial online behaviours. Further, these findings provide crucial information regarding the psychological profile of a “catfish” and may have important practical implications by informing the prevention and management of this online behaviour. © 2022 Elsevier Ltd

Classification of Twitter users with eating disorder engagement : learning from the biographies

- Abuhassan, Mohammad, Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Shatte, Adrian, Liu, Chengfei, Sukunesan, Suku

Authors: Abuhassan, Mohammad , Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Shatte, Adrian , Liu, Chengfei , Sukunesan, Suku
Date: 2023
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 140, no. (2023), p.
Full Text: false
Reviewed:
Description: Individuals with an Eating Disorder (ED) are typically reluctant to seek help via traditional means (e.g., psychologists). However, recent evidence suggests that many individuals seek assistance via social media for weight and diet related concerns. Sophisticated approaches are needed to better distinguish those who may be in need of help for an ED from those who are simply commenting on ED in online social environments. In order to facilitate effective communication between individuals with or at-risk of an ED and healthcare professionals, this research exploits a deep learning model to differentiate the users with ED engagement (e.g., ED sufferers, healthcare professionals or communicators) over social media. For this purpose, a collection of Twitter data is compiled using Twitter application programming interface (API) on the Australian Research Data Commons (ARDC) Nectar research cloud. After collecting 1,400,000 Twitter biographies in total, a subset of 4000 biographies are annotated manually. This annotation enables the differentiation of users engaged with ED-focused language on social media into five categories: ED-user, healthcare professional, communicator, healthcare professional-communicator, and other. Based on these annotated categories, a predictive deep learning model based on bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is developed. The model achieves an F1 score of 98.19% and an accuracy of 98.37%. It demonstrates the viability of detecting the individuals with possible ED risk and distinguishes them from other categories using their biography data. We further conducted a network analysis for investigating the communication network between these categories. Our analysis shows that ED-users are more secretive and self-protective, whereas the healthcare professionals and communicators frequently interact with each other and a wide range of other people. To the best of our knowledge, our research is the first of its kind for identifying the different user categories engaged with ED-focused communications on social media. © 2022

The role of pre-existing knowledge and knowledge acquisition in internet-based cognitive-behavioural therapy for eating disorders

- Linardon, Jake, Broadbent, Jaclyn, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

Authors: Linardon, Jake , Broadbent, Jaclyn , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
Date: 2022
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 134, no. (2022), p.
Full Text: false
Reviewed:
Description: Knowledge is a relevant concept in internet-based cognitive-behaviour therapy (I-CBT), yet little research has sought to understand the role of knowledge in I-CBT for eating disorders. This study addressed this gap. Data were analysed from 293 participants enrolled in a RCT of I-CBT for eating disorder symptoms. A test assessing knowledge of CBT principles and eating disorders was administered before and after I-CBT. Participants had high knowledge to begin with, correctly answering 72% of items. A significant increase in knowledge scores and knowledge confidence was observed after ICBT. While no relationship between the degree of knowledge gain and the degree of symptom improvement emerged, an increase in confidence in one's knowledge was associated with greater symptom improvement. Higher baseline knowledge levels predicted lower likelihood of drop-out and a higher likelihood of adherence, but were unrelated to symptom-level improvement. Findings suggest that while new knowledge can be acquired through I-CBT, the degree of knowledge gain alone is not sufficient to explain improvement in symptoms. Pre-existing knowledge levels may be an important prognostic indicator of patient progress and compliance to I-CBT. Ensuring that patients can correctly apply the key I-CBT skills may be more important than knowledge gain. © 2022 Elsevier Ltd

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