A human-centered systematic literature review of the computational approaches for online sexual risk detection

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.Accepted manuscrip

A Human-Centered Approach to Improving Adolescent Online Sexual Risk Detection Algorithms

Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to ensure the broader societal impacts of this important work. In order to understand what and how adolescents talk about their online sexual interactions to inform study designs, a thematic content analysis of posts by adolescents on an online peer support mental health was conducted. Then, a user study and web-based platform, Instagram Data Donation (IGDD), was designed to create an ecologically valid dataset. Youth could donate and annotate their Instagram data for online risks. After participating in the study, an interview study was conducted to understand how youth felt annotating data for online risks. Based on private conversations annotated by participants, sexual risk detection classifiers were created. The results indicated Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level. Our experiments showed that classifiers trained on entire conversations performed better than message-level classifiers. We also trained classifiers to detect the severity risk level of a given message with CNN outperforming other models. We found that contextual (e.g., age, gender, and relationship type) and psycho-linguistic features contributed the most to accurately detecting sexual conversations. Our analysis provides insights into the important factors that enhance automated detection of sexual risks within youths\u27 private conversations

2019 EURēCA Abstract Book

Listing of student participant abstracts

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Movement: Journey of the Beat

Movement: Journey of the Beat addresses the trajectory and transition of popular culture through the modality of rhythm. It configures fresh narratives and new histories necessary to understand why auditory cultures have become increasingly significant in the digital age. Atomised and mobile technologies, which utilise sonic media through streaming, on-line radio and podcasts, have become ubiquitous in a post-work environment. These sonic media provide not merely the mechanisms of connection but also the contexts for understanding changing formations of both identity and community. This research addresses, through rhythm, how popular music culture, central to changing perceptions of ‘self’ and ‘others’ through patterns of production and consumption, must also be viewed as instrumental in shaping new platforms of communication that have resonance not only through the emergence of new social networks and cultural economies but also in the development of media literacies and pedagogic strategies. The shift to online technologies for cultural production and global consumption, although immersed in leisure practices, more significantly alludes to changing dynamics of power and knowledge. An online ecology represents a significant shift in the role of place and time in creative production and its subsequent access. Popular music invariably provides an entry point and subsequent platform for such shifts and this thesis looks to the rhythms within this popular culture in as much as they encode these transformations. This doctoral research builds on the candidate’s established career as music producer, broadcaster, journalist and teacher to construct an appropriate theoretical framework to indicate how the construction, transmission and consumption of popular music rhythms give an understanding of changing social contexts. The thesis maps the movement of commonly recognised popular rhythms from their places of construction to the spaces of reception within broader political, socio-economic and cultural frameworks. The thesis probes the contribution of place and time in transforming global cultures, via social geography and memory, positioning such changes within readings of mobility, stasis, modernity and technology. By consciously addressing multiple disciplines, from populist to academic, Movement provides evidence of how wider structural changes have become reified within the beat and how in turn rhythm provides an appropriate modality through which change can be negotiated and understood