TY - GEN
T1 - The Influence of Social Networking Usage Experience and Activity on Preferences of Explainable Artificial Intelligence (XAI) Representation Methods in a Hate Speech Detection System
AU - Al-Ansari, Noor
AU - Al-Thani, Dena
AU - Bahameish, Mariam
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2024/12/3
Y1 - 2024/12/3
N2 - Hate speech is widespread across social media platforms, where users can influence others positively or negatively through simple actions like typing. These platforms use AI to personalize content, and explainable AI (XAI) is integrated to clarify AI decision-making for users. While progress has been made in aligning XAI with user expectations through human-computer interaction (HCI) methods, the study of hate speech detection within XAI remains limited. This study investigated how social media experience and activity influence user preferences for XAI representation methods. Focusing on the Arab world, it introduces a high-fidelity prototype Arabic hate speech detection system and evaluates preferred XAI methods. Data were collected through subjective and objective measures, including Social Media Activity and Social Networking Usage Questionnaires on user preferences of XAI representation methods. The results show that active social media use correlates with social networking experience (SNUQpositive: r = .40, SNUQnegative = .23) and time spent on platforms (r = .22); however, mediation analysis found that hours spent did not influence XAI preference. Future research could explore additional factors affecting XAI preferences.
AB - Hate speech is widespread across social media platforms, where users can influence others positively or negatively through simple actions like typing. These platforms use AI to personalize content, and explainable AI (XAI) is integrated to clarify AI decision-making for users. While progress has been made in aligning XAI with user expectations through human-computer interaction (HCI) methods, the study of hate speech detection within XAI remains limited. This study investigated how social media experience and activity influence user preferences for XAI representation methods. Focusing on the Arab world, it introduces a high-fidelity prototype Arabic hate speech detection system and evaluates preferred XAI methods. Data were collected through subjective and objective measures, including Social Media Activity and Social Networking Usage Questionnaires on user preferences of XAI representation methods. The results show that active social media use correlates with social networking experience (SNUQpositive: r = .40, SNUQnegative = .23) and time spent on platforms (r = .22); however, mediation analysis found that hours spent did not influence XAI preference. Future research could explore additional factors affecting XAI preferences.
KW - Explainable artificial intelligence
KW - hate speech
KW - XAI preference
UR - http://www.scopus.com/inward/record.url?scp=85211955639&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-0567-5_6
DO - 10.1007/978-981-96-0567-5_6
M3 - Conference contribution
AN - SCOPUS:85211955639
SN - 9789819605668
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 67
EP - 77
BT - Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings
A2 - Barhamgi, Mahmoud
A2 - Wang, Hua
A2 - Wang, Xin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Web Information Systems Engineering, WISE 2024
Y2 - 2 December 2024 through 5 December 2024
ER -