{"id":338,"date":"2018-02-13T03:49:48","date_gmt":"2018-02-13T03:49:48","guid":{"rendered":"http:\/\/wordpress.cs.vt.edu\/cs6724spring18\/?p=338"},"modified":"2018-02-13T03:49:48","modified_gmt":"2018-02-13T03:49:48","slug":"reflection-7-02-13-ashish-baghudana","status":"publish","type":"post","link":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/2018\/02\/13\/reflection-7-02-13-ashish-baghudana\/","title":{"rendered":"Reflection #7 \u2013 [02\/13] \u2013 Ashish Baghudana"},"content":{"rendered":"
Niculae, Vlad, et al. “Linguistic harbingers of betrayal: A case study on an online strategy game.” arXiv preprint arXiv:1506.04744<\/i> (2015).<\/div>\n

In very much the same vein as The Language that Gets People to Give: Phrases that Predict Success on Kickstarter<\/span><\/em> by Mitra et al.<\/em>, the authors in this paper look for linguistic cues that foretell betrayal in relationships. Their research focuses on the online game Diplomacy<\/strong> that is set in the pre-World War 1 era. An important aspect of this paper is understanding the game and its intricacies. Each player chooses a country, forms alliances with other players, and tries to win the game by capturing different territories in Europe. Central to the game are these alliances<\/em> and betrayals<\/em>,\u00a0and the conversations that happen when a\u00a0player becomes disloyal to a friend.<\/p>\n

The paper uses draws on prior research work in extracting politeness<\/em>, sentiment<\/em>, and linguistic cues for several of its features, and it was instructive to see the uses of some of these social computing tools in their research.<\/p>\n

The authors find that there are subtle signs that predict betrayal, namely:<\/p>\n

    \n
  1. An imbalance of positive sentiment before the betrayal, where the betrayer uses more positive sentiment;<\/li>\n
  2. Less argumentation and discourse from the betrayer;<\/li>\n
  3. Less planning markers in the betrayer’s language;<\/li>\n
  4. More polite behavior from the betrayer; and<\/li>\n
  5. An imbalance in the number of messages exchanged<\/li>\n<\/ol>\n

    Intuitively, I can relate to observations #2, #3, and #5. However, positive sentiment and polite behavior would perhaps not indicate betrayal in an offline context. I do wish that these results were explained better and more examples given to indicate why they made sense.<\/p>\n

    I also felt that the machine learning model to predict betrayal could have been described better. I could not immediately understand their feature extraction mechanism — were linguistic cues used as binary features or count features?<\/em> Assuming it wasn’t a thin-slicing study and they used count features, did they normalize the counts over the number of times two players spoke?<\/em> Additionally, they compared the performance of their model against the players (who were never able to predict a betrayal, i.e. their accuracy was 0%). While 0% -> 57% seems like a big jump, the machine learning model could have predicted at random and still obtained a 50% accuracy rate. This begs the question of how accurate the model really is and what features it found important.<\/p>\n

    Papers in computational social science often need to define (otherwise abstract) social constructs precisely, and quantitatively. Niculae et al. attempt to define friendships, alliances, and betrayals in this paper. While I like and agree with their definitions with respect to the game, it is important to recognize that these definitions are not necessarily generalizable. The paper studies a small subset of relationships online<\/em>. I would be interested in seeing how this could be replicated for more offline<\/em> contexts.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Niculae, Vlad, et al. “Linguistic harbingers of betrayal: A case study on an online strategy game.” arXiv preprint arXiv:1506.04744 (2015). In very much the same vein as The Language that Gets People to Give: Phrases that Predict Success on Kickstarter by Mitra et al., the authors in this paper look for linguistic cues that foretell […]<\/p>\n","protected":false},"author":118,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-338","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/338","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/users\/118"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/comments?post=338"}],"version-history":[{"count":1,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/338\/revisions"}],"predecessor-version":[{"id":339,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/338\/revisions\/339"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/media?parent=338"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/categories?post=338"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/tags?post=338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}