“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media

Capozzi, Arthur; Lai, Mirko; Basile, Valerio; Poletto, Fabio; Sanguinetti, Manuela; Bosco, Cristina; Patti, Viviana; Ruffo, Giancarlo; Musto, Cataldo; Polignano, Marco; Semeraro, Giovanni; Stranisci, Marco

doi:10.4000/ijcol.659

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Another relevant system for monitoring social media is Contro L'Odio [35], which monitors and contrasts discrimination and hate speech in Italy. This platform also has several visualizations that allow a user to reduce the complexity of the data.…”

Section: Discussionmentioning

confidence: 99%

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Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

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Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

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Section: Discussionmentioning

confidence: 99%

“…Several works have been particularly relevant for the monitoring of hate speech. Contro l'odio [35] is a web platform for monitoring immigrants' discrimination and hate speech in Italy. It provides the results of linguistic analysis in interactive maps.…”

Section: From Radicalization Drivers To Radicalization Signalsmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

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“…At the other end of the spectrum from analyses based solely on machine learning is Amnesty International Italia's Hate Barometer (2021), which was developed using manual annotation by activists and only uses automated tools to test the level of significance. 6 Major detection projects combining algorithmic logic and manual annotation include: 'Contro l'odio' developed by the Acmos Association with the University of Turin and the University of Bari (Capozzi et al, 2020;Poletto et al, 2021) (Novak et al, 2020) 9 .…”

Section: Hate Studies In Italymentioning

confidence: 99%

Hate Speech Research: Algorithmic and Qualitative Evaluations. A Case Study of Anti-Gypsy Hate on Twitter

Pasta

2023

Research on Education and Media

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Hate speech may be the research focus of the interdisciplinary field of hate studies, but it is also a difficult phenomenon to define. Internationally, there are several detection studies on automatically detecting hate speech. They can be grouped according to two approaches: the first includes searching using only machine learning methods, while the second includes studies that combine automatic searching with human classification. The case study on anti-Gypsy hate in Italian on Twitter in the second half of 2020 falls into the second category, and its methods are outlined here. Based on the results (annotation as ‘hate’/‘non-hate’, identification of forms of rhetoric and anti-Gypsyism), the researchers propose classifying online content according to seven indicators called the ‘spectrum of online hate’.

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“…one composed of tweets (TEST_TW) and another composed of news headlines (TEST_TW), whereas the training set (TRAIN_TW) of HASPEEDE2020 consists only of tweets. About the composition of TRAIN_TW and TEST_TW, a part of tweets comes from the Twitter dataset released in the first edition of HaSpeeDe in 2018 (and partially derived from IHSC); the rest instead has been gathered for the Italian hate speech monitoring project "Contro l'Odio" (Capozzi et al 2019). In particular, only data posted between September and December 2018 were included in TRAIN_TW, whereas TEST_TW contains the tweets posted between January and May 2019.…”

Section: Description Of the Datasetmentioning

confidence: 99%

Killing me softly: Creative and cognitive aspects of implicitness in abusive language online

2022

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Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims’ life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.

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“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media

Cited by 11 publications

References 27 publications

Making Sense of Language Signals for Monitoring Radicalization

Making Sense of Language Signals for Monitoring Radicalization

Hate Speech Research: Algorithmic and Qualitative Evaluations. A Case Study of Anti-Gypsy Hate on Twitter

Killing me softly: Creative and cognitive aspects of implicitness in abusive language online

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