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Why do we Hate Migrants?: A Double Machine Learning-based Approach

Published: 05 September 2023 Publication History

Abstract

Abstract: AI-based NLP literature has explored antipathy toward the marginalized section of society, such as migrants, and their social acceptance. Broadly, extant literature has conceptualized this as an online hate speech detection task and employed predictive ML models. However, a crucial omission in this literature is the genesis (or causality) of online hate, i.e., why do we hate migrants? Drawing insights from social science literature, we have identified three antecedents of online hate: Cultural, Economic, and Security concerns. Subsequently, we probe -which of these concerns triggers higher toxicity on online platforms? Initially, we consider OLS-based regression analysis and SHAP framework to identify the predictors of toxicity, and subsequently, we use Double Machine Learning (DML)-based casual analysis to investigate whether good predictors of toxicity are also causally significant. We find that the causal effect of Cultural concerns on toxicity is higher than Security and Economic concerns.

References

[1]
Nabil Ahmed, Anna Marriott, Nafkote Dabi, Megan Lowthers, Max Lawson, and Leah Mugehera. 2022. Inequality kills: The unparalleled action needed to combat unprecedented inequality in the wake of COVID-19. (2022).
[2]
Uttara M Ananthakrishnan and Catherine E Tucker. 2022. The drivers and virality of hate speech online. Available at SSRN 3793801 (2022).
[3]
Carlos Arcila-Calderón, David Blanco-Herrero, Maximiliano Frías-Vázquez, and Francisco Seoane-Pérez. 2021. Refugees welcome? Online hate speech and sentiments in Twitter in Spain during the reception of the boat Aquarius. Sustainability 13, 5 (2021), 2728.
[4]
Carlos Arcila-Calderón, Patricia Sánchez-Holgado, Cristina Quintana-Moreno, Javier J Amores, and David Blanco-Herrero. 2022. Hate speech and social acceptance of migrants in Europe: analysis of tweets with geolocation//Discurso de odio y aceptación social hacia migrantes en Europa: análisis de tuits con geolocalización. Comunicar 30, 71 (2022), 21--35.
[5]
Lisa Argyle, Ian Gray, Matti Nelimarkka, and Rochelle Terman. 2016. Cultural Threats and Islamophobia in American News Media. In NCAPSA American Politics Workshop. 1--44.
[6]
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th international workshop on semantic evaluation. 54--63.
[7]
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, and Vasilis Syrgkanis. 2019. EconML: a Python package for ML-based heterogeneous treatment effects estimation. GitHub (2019).
[8]
Carlos Arcila Calderón, David Blanco-Herrero, and María Belén Valdez Apolo. 2020. Rejection and hate speech in Twitter: Content analysis of Tweets about migrants and refugees in Spanish. Revista Española de Investigaciones Sociológicas (REIS) 172, 172 (2020), 21--56.
[9]
Erik Cambria, Björn Schuller, Yunqing Xia, and Catherine Havasi. 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems 28, 2 (2013), 15--21.
[10]
Noah Carl. 2016. Net opposition to immigrants of different nationalities correlates strongly with their arrest rates in the UK. Open Quantitative Sociology & Political Science (2016).
[11]
Eshwar Chandrasekharan, Shagun Jhaver, Amy Bruckman, and Eric Gilbert. 2022. Quarantined! Examining the effects of a community-wide moderation intervention on Reddit. ACM Transactions on Computer-Human Interaction (TOCHI) 29, 4 (2022), 1--26.
[12]
Eshwar Chandrasekharan, Umashanthi Pavalanathan, Anirudh Srinivasan, Adam Glynn, Jacob Eisenstein, and Eric Gilbert. 2017. You can't stay here: The efficacy of reddit's 2015 ban examined through hate speech. Proceedings of the ACM on Human-Computer Interaction 1, CSCW (2017), 1--22.
[13]
Hsinchun Chen and David Zimbra. 2010. AI and opinion mining. IEEE Intelligent Systems 25, 3 (2010), 74--80.
[14]
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. 2017. Double/debiased/neyman machine learning of treatment effects. American Economic Review 107, 5 (2017), 261--265.
[15]
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. Double/debiased machine learning for treatment and structural parameters.
[16]
Phillip Connor and Jens Manuel Krogstad. 2018. Many worldwide oppose more migration--both into and out of their countries. (2018).
[17]
Lana Cuthbertson, Alex Kearney, Riley Dawson, Ashia Zawaduk, Eve Cuthbertson, Ann Gordon-Tighe, and Kory W Mathewson. 2019. Women, politics and Twitter: Using machine learning to change the discourse. arXiv preprint arXiv:1911.11025 (2019).
[18]
Mattias Ekman. 2018. Anti-refugee mobilization in social media: The case of soldiers of Odin. Social Media+ Society 4, 1 (2018), 2056305118764431.
[19]
Mai ElSherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, and Elizabeth Belding. 2018. Hate lingo: A target-based linguistic analysis of hate speech in social media. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 12.
[20]
Lee Fiorio, Guy Abel, Jixuan Cai, Emilio Zagheni, Ingmar Weber, and Guillermo Vinué. 2017. Using twitter data to estimate the relationship between short-term mobility and long-term migration. In Proceedings of the 2017 ACM on web science conference. 103--110.
[21]
Paula Fortuna, Juan Soler, and Leo Wanner. 2020. Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets. In Proceedings of the 12th language resources and evaluation conference. 6786--6794.
[22]
Antigoni-Maria Founta and Lucia Specia. 2021. A Survey of Online Hate Speech through the Causal Lens. arXiv preprint arXiv:2109.08120 (2021).
[23]
Pankaj Ghemawat and S Altman. 2013. Depth index of globalization--And the big shift to emerging economies. Pankaj Ghemawat/IESE Report, University of Navarra, Spain (2013).
[24]
Jeanine PD Guidry, Lucinda L Austin, Kellie E Carlyle, Karen Freberg, Michael Cacciatore, Shana Meganck, Yan Jin, and Marcus Messner. 2018. Welcome or not: Comparing# refugee posts on Instagram and Pinterest. American Behavioral Scientist 62, 4 (2018), 512--531.
[25]
Samuel S Guimarães, Julio CS Reis, Filipe N Ribeiro, and Fabrício Benevenuto. 2020. Characterizing toxicity on facebook comments in brazil. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 253--260.
[26]
Asmelash Teka Hadgu, Kaweh Djafari Naini, and Claudia Niederée. 2016. Welcome or not-welcome: Reactions to refugee situation on social media. arXiv preprint arXiv:1610.02358 (2016).
[27]
Paul Hünermund, Beyers Louw, and Itamar Caspi. 2021. Double Machine Learning and Automated Confounder Selection--A Cautionary Tale. arXiv preprint arXiv:2108.11294 (2021).
[28]
Katherine A Keith, David Jensen, and Brendan O'Connor. 2020. Text and causal inference: A review of using text to remove confounding from causal estimates. arXiv preprint arXiv:2005.00649 (2020).
[29]
Aparup Khatua and Wolfgang Nejdl. 2021. Analyzing European Migrant-related Twitter Deliberations. In Companion Proceedings of the Web Conference 2021. 166--170.
[30]
Aparup Khatua and Wolfgang Nejdl. 2021. Struggle to Settle down! Examining the Voices of Migrants and Refugees on Twitter Platform. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. 95--98.
[31]
Aparup Khatua and Wolfgang Nejdl. 2022. Endorsement Analysis of Migrant-related Deliberations on YouTube: Prior to and During 2022 Ukrainian crisis. In Open Challenges in Online Social Networks. 31--38.
[32]
Aparup Khatua and Wolfgang Nejdl. 2022. Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter. In Proceedings of the 33rd ACM Conference on Hypertext and Social Media. 214--219.
[33]
Aparup Khatua and Wolfgang Nejdl. 2022. Unraveling Social Perceptions & Behaviors towards Migrants on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 16. 512--523.
[34]
Aparup Khatua, Emilio Zagheni, and Ingmar Weber. 2023. Host-Centric Social Connectedness of Migrants in Europe on Facebook. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 17. 1143--1147.
[35]
Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, et al. 2022. A Causal AI Suite for Decision-Making. In NeurIPS 2022 Workshop on Causality for Real-world Impact.
[36]
Ramona Kreis. 2017. # refugeesnotwelcome: Anti-refugee discourse on Twitter. Discourse & Communication 11, 5 (2017), 498--514.
[37]
Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5, 1 (2012), 1--167.
[38]
Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
[39]
Vera Messing and Bence Ságvári. 2018. Looking behind the culture of fear. Cross-national analysis of attitudes towards migration. (2018).
[40]
Karsten Müller and Carlo Schwarz. 2020. From hashtag to hate crime: Twitter and anti-minority sentiment. Available at SSRN 3149103 (2020).
[41]
Kevin Munger. 2017. Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior 39 (2017), 629--649.
[42]
Alberto Nardelli and George Arnett. 2014. Today's key fact: you are probably wrong about almost everything. The Guardian 29 (2014).
[43]
Adina Nerghes and Ju-Sung Lee. 2018. The refugee/migrant crisis dichotomy on Twitter: A network and sentiment perspective. In Proceedings of the 10th ACM conference on web science. 271--280.
[44]
Adewale Obadimu, Esther Mead, Muhammad Nihal Hussain, and Nitin Agarwal. 2019. Identifying toxicity within youtube video comment. In Social, Cultural, and Behavioral Modeling: 12th International Conference, SBP-BRiMS 2019, Washington, DC, USA, July 9-12, 2019, Proceedings 12. Springer, 214--223.
[45]
Alexandra Olteanu, Carlos Castillo, Jeremy Boy, and Kush Varshney. 2018. The effect of extremist violence on hateful speech online. In Proceedings of the international AAAI conference on web and social media, Vol. 12.
[46]
Nazan Öztürk and Serkan Ayvaz. 2018. Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics 35, 1 (2018), 136--147.
[47]
Michelle Peterie and David Neil. 2020. Xenophobia towards asylum seekers: A survey of social theories. Journal of Sociology 56, 1 (2020), 23--35.
[48]
Fabio Poletto, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, and Viviana Patti. 2021. Resources and benchmark corpora for hate speech detection: a systematic review. Language Resources and Evaluation 55 (2021), 477--523.
[49]
Migration Data Portal. 2023. Migration statistics. https://www.migrationdataportal.org/. [Online; accessed 15-March-2023].
[50]
A Rakotonarivo. 2020. Who are the women on the move? A portrait of female migrant workers. ILO, 18 December 2020.
[51]
Jill Walker Rettberg and Radhika Gajjala. 2016. Terrorists or cowards: negative portrayals of male Syrian refugees in social media. Feminist Media Studies 16, 1 (2016), 178--181.
[52]
Paul R Rosenbaum and Donald B Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 1 (1983), 41--55.
[53]
Paul R Rosenbaum and Donald B Rubin. 1984. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American statistical Association 79, 387 (1984), 516--524.
[54]
Nazanin Salehabadi, Anne Groggel, Mohit Singhal, Sayak Saha Roy, and Shirin Nilizadeh. 2022. User Engagement and the Toxicity of Tweets. arXiv preprint arXiv:2211.03856 (2022).
[55]
Manuela Sanguinetti, Fabio Poletto, Cristina Bosco, Viviana Patti, and Marco Stranisci. 2018. An italian twitter corpus of hate speech against immigrants. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
[56]
Eugenia Siapera, Moses Boudourides, Sergios Lenis, and Jane Suiter. 2018. Refugees and network publics on Twitter: Networked framing, affect, and capture. Social Media+ Society 4, 1 (2018), 2056305118764437.
[57]
John Sides and Jack Citrin. 2007. European opinion about immigration: The role of identities, interests and information. British journal of political science 37, 3 (2007), 477--504.
[58]
Richard Stansfield and Brenna Stone. 2018. Threat perceptions of migrants in Britain and support for policy. Sociological Perspectives 61, 4 (2018), 592--609.
[59]
Shiliang Sun, Chen Luo, and Junyu Chen. 2017. A review of natural language processing techniques for opinion mining systems. Information fusion 36 (2017), 10--25.
[60]
Jiliang Tang, Yi Chang, and Huan Liu. 2014. Mining social media with social theories: a survey. ACM Sigkdd Explorations Newsletter 15, 2 (2014), 20--29.
[61]
Pamela Bilo Thomas, Daniel Riehm, Maria Glenski, and Tim Weninger. 2021. Behavior change in response to subreddit bans and external events. IEEE Transactions on Computational Social Systems 8, 4 (2021), 809--818.
[62]
Emma von Essen and Joakim Jansson. 2020. Misogynistic and xenophobic hate language online: a matter of anonymity. (2020).
[63]
Emilio Zagheni, Venkata Rama Kiran Garimella, Ingmar Weber, and Bogdan State. 2014. Inferring international and internal migration patterns from twitter data. In Proceedings of the 23rd international conference on world wide web. 439--444.
[64]
Daniel Zeng, Hsinchun Chen, Robert Lusch, and Shu-Hsing Li. 2010. Social media analytics and intelligence. IEEE Intelligent Systems 25, 6 (2010), 13--16.

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cover image ACM Conferences
HT '23: Proceedings of the 34th ACM Conference on Hypertext and Social Media
September 2023
334 pages
ISBN:9798400702327
DOI:10.1145/3603163
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Published: 05 September 2023

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Author Tags

  1. Causality
  2. Double machine Learning
  3. Online Hate
  4. Toxicity

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  • Refereed limited

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  • Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor

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