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The Transition from Life to the Digital Afterlife
Published in Maggi Savin-Baden, Victoria Mason-Robbie, Digital Afterlife, 2020
On the other hand, there are times when the presence of strangers can be extremely distressing. According to Phillips (2015), memorial trolling or RIP trolling occurs when online instigators post abusive comments or insensitive images on SNS or social media. Her interviews with memorial trolls revealed that different beliefs about the appropriateness of publicly sharing one's grief, particularly by individuals who did not personally know the deceased, sometimes called ‘grief tourists’, appear to be at the heart of this phenomenon. Unlike this author's appreciation of the expression of ‘experiential empathy’ as beneficial to both the ‘stranger’ and the recipient, trolls described this behaviour as disingenuous, motivated by boredom, and ‘a pathological need for attention masquerading as grief’ (p. 84).
Supervised Learning for Aggression Identification and Author Profiling over Twitter Dataset
Published in Sk Md Obaidullah, KC Santosh, Teresa Gonçalves, Nibaran Das, Kaushik Roy, Document Processing Using Machine Learning, 2019
For any machine learning approach, the dataset plays an important role. The dataset used here was obtained from the shared task on “Aggression Identification” [11] of a workshop named “Trolling, Aggression and Cyberbullying” [12]. The dataset had 15,000 Facebook posts and comments written in Hindi (in both Roman and Devanagari script) and English. They were annotated with three labels, namely “Overtly Aggressive (OAG)”, “Covertly Aggressive (CAG)” or “Non-aggressive (NAG)”. The idea here was to predict the label of an unknown post or comment using supervised learning. Before any further processing, data was visualized for better understanding and system modeling. There are two data files for each language. “en_train/hi_train.csv” contains the train set, with 12,000 records. (Overtly Aggressive = 2708, Covertly Aggressive = 4240 and Non-aggressive = 5052). “en_dev/hi_dev.csv” contains the test set with 3001 records. (Overtly Aggressive = 711, Covertly Aggressive = 1057 and Non-aggressive = 1233).
Topic-Based Classification for Aggression Detection in a Social Network
Published in Archana Singh, Vinod Kumar Shukla, Ashish Seth, A. Sai Sabitha, ICT and Data Sciences, 2022
Karanjot Singh, Sonia Saini, Ruchika Bathla, Vinod Kumar Shukla, Ritu Punhani, Divi Anand
“Benchmarking Aggression Identification in Social Media” by Kumar et al. (2018) is a research paper that focuses on presenting report and discoveries of the Shared Task about Aggression Identification composed like a major aspect of the first Workshop of Trolling, Aggression, and cyberbullying (TRAC – 1).
Psychopathy, impulsivity, and internet trolling: role of aggressive humour
Published in Behaviour & Information Technology, 2022
Tuğba Türk Kurtça, İbrahim Demirci
A potential attacker reduces criminality to daily life when there is a proper target and a lack of guardians (Cohen and Felson 1979). Because trolling includes deceptive and harmful attempts (Golf-Papez and Veer 2017), trolling should not be underestimated (Coles and West 2016). Victims of trolling might face psychological, legal, and social problems. Despite the negative consequences of trolling, according to Cheng and colleagues (2017), everyone is susceptible to trolling in proper circumstances. Investigating the traits that end up in trolling might be instructive for studies that try to prevent trolling. In this study, the model in which psychopathy and impulsivity predict aggressive humour, and aggressive humour predicts iTrolling will be tested. After a literature search, only a few studies were encountered that investigate dark triad and humour (e.g. Navarro-Carrillo, Torres-Marín, and Carretero-Dios 2021).