{"id":35473,"date":"2021-10-27T22:19:45","date_gmt":"2021-10-27T20:19:45","guid":{"rendered":"https:\/\/www.unhcr.org\/innovation\/?p=35473"},"modified":"2021-10-27T22:27:07","modified_gmt":"2021-10-27T20:27:07","slug":"text-analytics-for-online-hate-speech-monitoring-and-protection","status":"publish","type":"post","link":"https:\/\/www.unhcr.org\/innovation\/text-analytics-for-online-hate-speech-monitoring-and-protection\/","title":{"rendered":"Text analytics for online hate speech monitoring and protection"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;3.22.7&#8243; custom_padding=&#8221;0px||0px|||&#8221;][et_pb_row custom_padding=&#8221;||0px|||&#8221; _builder_version=&#8221;3.22.7&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.22.7&#8243;][et_pb_text _builder_version=&#8221;3.22.7&#8243; custom_padding=&#8221;||32px|||&#8221;]<\/p>\n<p>In 2016, the Innovation Service and <span><a href=\"https:\/\/www.unglobalpulse.org\/about\/\">UN Global Pulse <\/a><\/span>used data from Twitter to monitor protection issues for the first time using artificial intelligence to understand the interactions between refugees, host communities, and service providers during movement through Europe. UNHCR\u2019s Innovation Service\u00a0 continued exploring text analytics, open-source and commercial tools, as well as with different computer science techniques to be able to quantify, analyse and understand issues related to UNHCR\u2019s protection mandate. For example, protection-related incidents such as hate speech, discrimination and xenophobia against refugees and asylum seekers, as well as other persons of concern to UNHCR are commonly expressed online.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;3.22.7&#8243; custom_padding=&#8221;1px||0px|||&#8221;][et_pb_row custom_padding=&#8221;|32px||32px||true&#8221; make_equal=&#8221;on&#8221; _builder_version=&#8221;3.22.7&#8243; background_color=&#8221;#f4f4f4&#8243; border_radii=&#8221;on|8px|8px|8px|8px&#8221;][et_pb_column type=&#8221;1_3&#8243; _builder_version=&#8221;3.22.7&#8243;][et_pb_text module_class=&#8221;grey&#8221; _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<h2>Humanitarian challenge<\/h2>\n<p>Incorporating text data to monitor humanitarian crises and perceptions about refugees<\/p>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_3&#8243; _builder_version=&#8221;3.22.7&#8243;][et_pb_text _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<h2>Challenge Question<\/h2>\n<p><em>How can UNHCR teams improve their efficiency in the analysis of large volumes of text-based data, including high velocity data such as social media? <\/em><\/p>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_3&#8243; _builder_version=&#8221;3.22.7&#8243;][et_pb_text _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<h2>Data innovation solution<\/h2>\n<p>Open-source tools and techniques (e.g. topics modeling, natural language processing, NLP) and Licensed Social Media Listening tools using public APIs.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;3.22.7&#8243; custom_padding=&#8221;0px|||||&#8221;][et_pb_row custom_padding=&#8221;30px|||||&#8221; _builder_version=&#8221;3.22.7&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.22.7&#8243;][et_pb_toggle title=&#8221;Papers and Citations&#8221; _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/innovation\/wp-content\/uploads\/2017\/09\/FINAL-White-Paper.pdf\">Social Media and Forced Displacement: Big Data Analytics &amp; Machine-Learning<\/a>.<\/p>\n<p><a href=\"https:\/\/www.unglobalpulse.org\/project\/understanding-perceptions-of-migrants-and-refugees-with-social-media\/\">Understanding Perceptions of Migrants and Refugees in Europe with Social Media<\/a><\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/protection\/operations\/5f7c860f4\/unhcr-guidance-on-racism-and-xenophobia.html\">UNHCR Guidance on Racism and Xenophobia<\/a><\/p>\n<p><a href=\"https:\/\/migrationdataportal.org\/data-innovation-31\">Exploring the use of social media data and artificial intelligence to analyse perceptions of refugees<\/a><\/p>\n<p><a href=\"https:\/\/journals.sagepub.com\/doi\/full\/10.1177\/0267323119886151\">Anti-immigration and racist discourse in social media<\/a><\/p>\n<p>[\/et_pb_toggle][et_pb_toggle title=&#8221;Media Outlets&#8221; _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<p><a href=\"https:\/\/apolitical.co\/en\/solution_article\/machine-learning-help-refugees-xenophobia\">How machine learning projects can tackle xenophobia and help refugees<\/a><\/p>\n<p><a href=\"https:\/\/qz.com\/africa\/1909232\/south-africa-s-xenophobia-protests-being-organized-on-twitter\/\">How social media is being exploited for South Africa\u2019s long-standing xenophobia problem<\/a><\/p>\n<p><a href=\"https:\/\/en.unesco.org\/news\/fight-against-xenophobia-age-disinformation-and-artificial-intelligence\">Fight against xenophobia in the age of disinformation and artificial intelligence<\/a><\/p>\n<p><a href=\"https:\/\/www.unglobalpulse.org\/2016\/09\/twitter-and-un-global-pulse-announce-data-partnership\/\">Twitter and UN Global Pulse announce data partnership<\/a><\/p>\n<p>[\/et_pb_toggle][et_pb_toggle title=&#8221;Blogs&#8221; _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/innovation\/teaching-robot-detect-xenophobia-online\/\">Teaching a \u2018robot\u2019 to detect xenophobia online<\/a><\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/innovation\/new-paper-from-un-global-pulse-and-unhcr-explores-use-of-digital-data-for-insights-into-forced-displacement\/\">New paper from UN Global Pulse and UNHCR explores use of digital data for insights into forced displacement<\/a><\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/5df9f0417.pdf\">Countering Toxic Narratives\u00a0 About Refugees And Migrants<\/a><\/p>\n<p><a href=\"https:\/\/www.unhcr.org\/innovation\/big-data-humanitarian-loop-algorithms\/\">From big data to humanitarian-in-the-loop algorithms<\/a><\/p>\n<p><a href=\"https:\/\/www.unglobalpulse.org\/microsite\/qatalog\/\">Qatalog: Query, Assign, Tag and Analyse<\/a><\/p>\n<p>[\/et_pb_toggle][et_pb_toggle title=&#8221;Code\/data repo&#8221; _builder_version=&#8221;3.22.7&#8243;]<\/p>\n<p><a href=\"https:\/\/www.unglobalpulse.org\/microsite\/qatalog\/\">Qatalog: Query, Assign, Tag and Analyse<\/a><\/p>\n<p><a href=\"https:\/\/www.thedeep.io\/\">The DEEP.io: the humanitarian secondary data review &amp; analysis platform<\/a><\/p>\n<p><a href=\"https:\/\/bedatadriven.github.io\/QualMiner\/\">Innovation Fund Project: QualMiner: Text mining for the Venezuelan Humanitarian Response in Ecuador<\/a><\/p>\n<p>[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In 2016, the Innovation Service and UN Global Pulse used data from Twitter to monitor protection issues for the first time using artificial 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