{"id":38512,"date":"2026-01-31T08:27:16","date_gmt":"2026-01-31T07:27:16","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/how-do-you-flag-low-effort-responses-that-arent-bots\/"},"modified":"2026-01-31T08:27:16","modified_gmt":"2026-01-31T07:27:16","slug":"how-do-you-flag-low-effort-responses-that-arent-bots","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/how-do-you-flag-low-effort-responses-that-arent-bots\/","title":{"rendered":"How Do You Flag Low-effort Responses That Aren&#8217;t Bots?"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Bot detection is relatively straightforward these days (honeypots, timestamps, etc.). But I\u2019m struggling with a different data quality issue: The &#8220;Bored Human.&#8221;<\/p>\n<p>These are real people who technically pass the bot checks but select &#8220;C&#8221; for every answer or type &#8220;good&#8221; in every text box just to finish.<\/p>\n<p>When cleaning a new dataset, what are your heuristics for flagging these? Do you look for standard deviation in their answers (straight-lining), or do you rely on minimum character counts for open text?<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/EnergyBrilliant540\"> \/u\/EnergyBrilliant540 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1qirxq4\/how_do_you_flag_loweffort_responses_that_arent\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1qirxq4\/how_do_you_flag_loweffort_responses_that_arent\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-38512 jlk' href='javascript:void(0)' data-task='like' data-post_id='38512' data-nonce='bc39e8310e' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-38512 lc'>0<\/span><\/a><\/div><\/div> <div class='status-38512 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Bot detection is relatively straightforward these days (honeypots, timestamps, etc.). But I\u2019m struggling with a different data&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-38512","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/38512","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=38512"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/38512\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=38512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=38512"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=38512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}