{"id":9,"date":"2011-12-09T01:30:17","date_gmt":"2011-12-09T01:30:17","guid":{"rendered":"http:\/\/blogs.law.harvard.edu\/metaxas\/?p=9"},"modified":"2011-12-09T01:30:17","modified_gmt":"2011-12-09T01:30:17","slug":"predict-the-future","status":"publish","type":"post","link":"https:\/\/archive.blogs.harvard.edu\/metaxas\/2011\/12\/09\/predict-the-future\/","title":{"rendered":"Predict the Future!"},"content":{"rendered":"

The title may seem redundant. Of course if you\u00a0 are going to predict, you should predict the future — what else, predict the past? But, when referring to social media data it may not be that redundant. In recent years there has been an increase of research on social media data predicting the future<\/a>, predicting the present<\/a>, and predicting the past using knowledge acquired in the future<\/a>.<\/p>\n

Why is predicting important? Predicting is equivalent to intelligence<\/strong>, with an important qualification: We admire the intelligence of someone who can predict what is going to happen, but only when they can explain why<\/strong> they are able to do so. If one (e.g., an octopus<\/a>) is able to predict without explanation, we tend to downgrade it as coincidence.<\/p>\n

Earlier today, the Pew Research Center on Journalism<\/strong> published an analysis entitled “Twitter and the Campaign<\/a>“. They present a detailed study of millions of tweets and blogs, about what people say on social media about the candidates<\/strong> for the 2012<\/strong> elections<\/strong>. (Not too many nice things, it turns out, except for Ron Paul, who, at the same time, is trailing on the polls.)<\/p>\n

So, what does this mean for the predictive power of Twitter? Is he going to win because tweets have good things to say about him, or will he lose because tweets have good things to say about him? (Hint: The answer is “yes”.)<\/p>\n

\"\"<\/a>

Shepard Fairey meets Angry Birds: Poster of our 2011 ICWSM submission "Limits of Electoral Predictions using Twitter"<\/p><\/div>\n

Earlier this year, with my colleagues Eni Mustafaraj<\/a>, Dani Gayo-Avello<\/a> and student Catherine Lui we studied this question. Can one, analyzing social media data, predict the outcome of the US congressional elections? We did not find encouraging results, in neither the Google Trends<\/a> data nor the Twitter data<\/a> — thus the ingenious poster above that Dani designed.<\/p>\n

When it comes to something so important as the elections, social media will be<\/strong> manipulated, because the stakes are too high. One should keep that in mind as we get closer to election time and “news articles” will start appearing arguing that someone will win or lose based on the number of friends or followers this candidate has. If the author gets it right, he will make sure to remind us in the future. If he gets it wrong, he will forget it first.<\/p>\n

\"\"<\/a>

Today's mentally flexible tweet. Why is this important? What is special about the last 24 hours? Who is missing?<\/p><\/div>\n

This does not mean that nothing can be predicted using social media. Movie sales<\/a> can be predicted, as Bernando Huberman and his colleague showed. Flu outbreaks<\/a> and periodic sales<\/a> can be predicted, too. But not elections. At least without some sophisticated filtering that makes them as representative and competitive to the professional pollsters.<\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

The title may seem redundant. Of course if you\u00a0 are going to predict, you should predict the future — what else, predict the past? But, when referring to social media data it may not be that redundant. In recent years there has been an increase of research on social media data predicting the future, predicting […]<\/p>\n","protected":false},"author":4484,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[2689,56810,3261],"tags":[],"class_list":["post-9","post","type-post","status-publish","format-standard","hentry","category-elections","category-predicting","category-twitter"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p8GAZ2-9","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/posts\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/users\/4484"}],"replies":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":8,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/posts\/9\/revisions"}],"predecessor-version":[{"id":19,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/posts\/9\/revisions\/19"}],"wp:attachment":[{"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/media?parent=9"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/categories?post=9"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/metaxas\/wp-json\/wp\/v2\/tags?post=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}