Facebook psychology mood experiment probably bullshit but makes for good headlines shocker. Aileen youll like this as its a researchers getting excited about big data issue or a tweet or status update, however, this is a horrible analysis tool to use. That’s because it wasn’t designed to differentiate — and in fact, can’t differentiate — a negation word in a sentence.1 Let’s look at two hypothetical examples of why this is important. Here are two sample tweets (or status updates) that are not uncommon: “I am not happy.” “I am not having a great day.” An independent rater or judge would rate these two tweets as negative — they’re clearly expressing a negative emotion. That would be +2 on the negative scale, and 0 on the positive scale. But the LIWC 2007 tool doesn’t see it that way. Instead, it would rate these two tweets as scoring +2 for positive (because of the words “great” and “happy”) and +2 for negative (because of the word “not” in both texts). Kramer et al. (2014) found a 0.07% — that’s not 7 percent, that’s 1/15th of one percent!! — decrease in negative words in people’s status updates when the number of negative posts on their Facebook news feed decreased. Do you know how many words you’d have to read or write before you’ve written one less negative word due to this effect? Probably thousands. This isn’t an “effect” so much as a statistical blip that has no real-world meaning
Posted on: Mon, 30 Jun 2014 10:23:44 +0000
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