This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and a free app all around the world to elevate victims’ individual voices to find a societal solution. Hollaback! pretends to analyze the impact of a bystander during a harassment in order to launch a public awareness-raising campaign to equip everyday people with tools to undo harassment. Thus, the analysis presented in this paper is a first step in Hollaback!’s purpose: the automatic detection of a witness intervention inferred from the victim’s own report. In a first step, natural language processing techniques were used to analyze the victim’s free-text descriptions. For this part, we used the whole dataset with all its countries and locations. In addition, classification models, based on machine learning and soft computing techniques, were developed in the second part of this study to classify the descriptions into those that have bystander presence and those that do not. For this machine learning part, we selected the city of Madrid as an example, in order to establish a criterion of the witness behavior procedure.
Spanish layman's summary:
Este artículo analiza las descripciones en modo texto de situaciones de acoso recogidas de manera anónima en el proyecto Hollaback!. El objetivo principal es la detección automática de la intervención de testigos que se infiere a partir del informe de la víctima. Las herramientas utilizadas incluyen procesamiento de lenguaje natural y modelos de clasificación.
English layman's summary:
This paper analyzes textual descriptions of harassment situations collected anonymously by the Hollaback! project. The main objective is the automatic detection of a witness intervention inferred from the victim’s own report. The tools include Natural Language Processing and classification models.
Keywords: social violence; natural language processing; text classification; machine learning; harassment complaints; bystander presence
JCR Impact Factor and WoS quartile: 2.679 - Q2 (2020)
DOI reference: 10.3390/app11178007
Published on-line: August 2021.
M. Alonso-Parra, C. Puente, A. Laguna, R. Palacios. Analysis of harassment complaints to detect witness intervention by machine learning and soft computing techniques. Applied Sciences. Vol. 11, nº. 17, pp. 8007-1 - 8007-16 August 2021 [Online]