{"id":494,"date":"2019-02-07T13:11:38","date_gmt":"2019-02-07T13:11:38","guid":{"rendered":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/?p=494"},"modified":"2019-02-07T13:11:39","modified_gmt":"2019-02-07T13:11:39","slug":"reading-reflection-4","status":"publish","type":"post","link":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/2019\/02\/07\/reading-reflection-4\/","title":{"rendered":"Reading Reflection #4"},"content":{"rendered":"\n<p><strong>Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination<\/strong><\/p>\n\n\n\n<p>In this paper, YouTube videos and comments were analyzed using\na multi-layered approach in order to try to observe trends related to hate,\nviolence, and discrimination. YouTube, a video sharing website, has brought up\nconcerns regarding its potential to be used as a platform for extremist actors to\nspread partisan and divisive content, which is sometimes untrue. Researchers\nlooked to explore whether popular right-wing YouTube channels exhibited\ndifferent patterns of hateful, violent, or discriminatory content, compared to\nbaseline channels. They collected 3731 right-wing videos and 5,072,728\ncorresponding comments, as well as 3942&nbsp;&nbsp;&nbsp;&nbsp; \nbaseline videos and 12,519590 corresponding comments. The following research questions\nwere asked:<\/p>\n\n\n\n<p>\u201cIs the presence of hateful vocabulary, violent content and discriminatory\nbiases more, less or equally accentuated in right-wing channels?\u201d<\/p>\n\n\n\n<p>\u201cAre, in general, commentators more, less or equally exacerbated\nthan video hosts in an effort to express hate and discrimination?\u201d<\/p>\n\n\n\n<p>A unique aspect of this paper was the three-layered approach.\nLexical analysis was used to carry out comparisons of semantic fields of words.\nTopic analysis was used to gather the prevalent topics in each group. Implicit\nbias analysis was used to measure implicit biases in the text. The researchers found\nthat the right-wing channels included a higher percentage of negative words such\nas \u201caggression, kill, rage, and violence\u201d. The baseline channels were found to include\na higher percentage of fields like joy and optimism. High evidence of hate was\nnot found for right-wing or baseline videos. Bothe categories were also found\nto exhibit a discriminatory bias towards Muslims. Comments were found to have\nmore words related to aggression, rage, and violence. Seventy-five percent of\nright-wing videos were shown to have more Muslim bias in the captions than the\ncomments.<\/p>\n\n\n\n<p>I liked the paper, although not much of the results surprised\nme. I struggled to understand what the researchers intended the impact to be.\nIn the conclusion they mentioned that these findings contribute to a better\nunderstanding of the behavior of general and right-wing YouTube users. They\nmentioned in the introduction that there are concerns about YouTube being used\nas an easy platform to spread hateful, violent, and discriminatory content, but\ndid not elaborate in the conclusion how their work impacts this concern. I\nthink that by knowing the trends that are present, more informed content sharing\ncan be done and steps can be taken by others to avoid encouraging hateful or\nharmful content sharing.<\/p>\n\n\n\n<p>I was surprised by the method of data collection. The\nresearchers used InfoWars as a seed and selected other channels favored by the\nfounder as the other right-wing videos. I thought that this process could have\nbeen done more methodically. In addition, they did not specify why they chose\nk=300 for the topic analysis. They may have gotten different results for\ndifferent values of k, and did not explain why they selected this as the best\noption.<\/p>\n\n\n\n<p>I like the three-layered approach and I like it could be\nuseful for other studies that involve text. Future work could include applying\nthese techniques to twitter or reddit data, or doing a similar study to this\nbut involving other topics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination In this paper, YouTube videos and comments were analyzed using a multi-layered approach in order to try to observe trends related to hate, violence, and discrimination. YouTube, a video sharing website, has brought up concerns regarding its potential to be used as a platform for extremist actors [&hellip;]<\/p>\n","protected":false},"author":240,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-494","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\/494","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\/240"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/comments?post=494"}],"version-history":[{"count":1,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts\/494\/revisions"}],"predecessor-version":[{"id":495,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/posts\/494\/revisions\/495"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/media?parent=494"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/categories?post=494"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs4984spring19\/wp-json\/wp\/v2\/tags?post=494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}