{"id":423,"date":"2019-02-06T04:44:36","date_gmt":"2019-02-06T04:44:36","guid":{"rendered":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/?p=423"},"modified":"2019-02-06T04:44:38","modified_gmt":"2019-02-06T04:44:38","slug":"reading-reflection-3-02-05-2019-chris-blair","status":"publish","type":"post","link":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/2019\/02\/06\/reading-reflection-3-02-05-2019-chris-blair\/","title":{"rendered":"Reading Reflection #3 \u2013 [02\/05\/2019] \u2013 [Chris Blair]"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">What I found most interesting about \u201cEarly Public Responses\nto the Zika-Virus on YouTube: Prevalence of and Differences Between Conspiracy\nTheory and Informational Videos\u201d is taking this concept and <strong>tangentially applying it to a different\nconcept<\/strong>. If we take <strong>disease\noutbreaks<\/strong>, even on the regional or town level and <strong>trace it into a machine learning algorithm<\/strong> we could find out trends\nthat would allow us to be even <strong>more\nproactive<\/strong> when it comes to detecting and preventing outbreaks. For example,\nrecent measles, mumps, and rubella outbreaks (https:\/\/www.who.int\/news-room\/detail\/29-11-2018-measles-cases-spike-globally-due-to-gaps-in-vaccination-coverage)\nhave been traced backed to the increased <strong>incidence\nof anti-vaccination conspiracy theories and their effect on populations to act<\/strong>\nand chose not to vaccinate their children. Using these principles, we can <strong>study social media activity<\/strong> on many\ndifferent and <strong>popular anti-vaccination\npages<\/strong> to target hotbeds where this disease could spread and <strong>push specific media campaigns<\/strong> to <strong>convince and dissuade people from joining\nthis damaging movement<\/strong>. Continuing on this, we can also use this for <strong>mental health awareness<\/strong> by using a specific\nmachine learning we a look at <strong>troubling\ntweets or Facebook posts made by individuals<\/strong> and analyze them in order to\ndetermine <strong>problem areas<\/strong> or even <strong>typical mental health triggers<\/strong>, be it\nenvironment, drugs, poverty, abuse, etc. we will be able to get a <strong>holistic view on the issues that are\nplaguing our society<\/strong> the most and bring them into the forefront. These are\njust two possible ideas but based on <strong>sentiment\nmapping<\/strong> we can also see how diseases spread commonly like the flu, is <strong>represented on social media, historically<\/strong>,\nand then be <strong>able to develop certain\nrules that allow us, using historical data to predict or see from a bird\u2019s eye\nperspective what the current condition of the outbreak<\/strong> is at and what we\ncan do to influence it. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What I found most interesting is that this project used a no\ncontext approach, not in the fact that the grammar had no context rather that <strong>they didn\u2019t consider the user who the words\nwere being said to<\/strong>. This inspired me with an idea which could be an\nextension and addition to this current paper, first we can use <strong>image recognition to determine the race\/ethnicity\nof someone<\/strong> then using that in order to figure out through the <strong>context given by the person receiving the\nhateful threats<\/strong> is actually hateful of like the paper was <strong>mentioning simply just part of a song<\/strong>. Continuing\nthis we can use <strong>certain classifiers like\nFacebook user profile to get people\u2019s religion<\/strong> as well and determine whether\nor not the user could be the recipient of hateful speech from that standpoint.\nHowever; after researching it seems like this is difficult more as many facial recognition\nsoftware has been <strong>trained substantially\non Caucasian models<\/strong>, so it seems like they are overtuned in that regard. This\npaper however does give us a <strong>great\nbaseline to work with in preventing hate speech<\/strong>, as we now have the grammar\nin order to figure out whether or not a certain tweet or post is in fact hate\nspeech, so now all we have to do is make the <strong>model more accurate by adding more context<\/strong>, maybe looking at the\nusers account who is <strong>posting the hate\nspeech to see if they have a history of posting hate speech<\/strong> or perhaps\nlooking at the <strong>person account to see if\nthey have a history of receiving hate speech<\/strong>. All this details we can use\nto make the <strong>model more accurate by\neliminating confounding factors<\/strong> like song lyrics, sarcasm, quoting, etc. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>What I found most interesting about \u201cEarly Public Responses to the Zika-Virus on YouTube: Prevalence of and Differences Between Conspiracy Theory and Informational Videos\u201d is taking this concept and tangentially applying it to a different concept. If we take disease outbreaks, even on the regional or town level and trace it into a machine learning [&hellip;]<\/p>\n","protected":false},"author":241,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-423","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts\/423","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/users\/241"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/comments?post=423"}],"version-history":[{"count":1,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts\/423\/revisions"}],"predecessor-version":[{"id":424,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts\/423\/revisions\/424"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/media?parent=423"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/categories?post=423"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/tags?post=423"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}