A lot of folks are pretty upset with Facebook for “manipulating” them. A recent study showed that Facebook adjusted the algorithm that posts notifications to your wall. At various times, they let through more “negative” posts. Other times they let through more “positive” posts. Then they checked the tone of your status updates to see whether your posts trended more positively or negatively in response.
This study has a lot of people concerned about emotional manipulation via social media. Should they be? Dr. John Grohol of PsychCentral says, “no.” Here’s his analysis.
…these kinds of studies often arrive at their findings by conducting language analysis on tiny bits of text. On Twitter, they’re really tiny — less than 140 characters. Facebook status updates are rarely more than a few sentences. The researchers don’t actually measure anybody’s mood.
So how do you conduct such language analysis, especially on 689,003 status updates? Many researchers turn to an automated tool for this, something called the Linguistic Inquiry and Word Count application (LIWC 2007). This software… was created to analyze large bodies of text — like a book, article, scientific paper, an essay written in an experimental condition, blog entries, or a transcript of a therapy session. Note the one thing all of these share in common — they are of good length, at minimum 400 words.
Why would researchers use a tool not designed for short snippets of text to, well… analyze short snippets of text? Sadly, it’s because this is one of the few tools available that can process large amounts of text fairly quickly.
Who Cares How Long the Text is to Measure?
You might be sitting there scratching your head, wondering why it matters how long the text it is you’re trying to analyze with this tool. One sentence, 140 characters, 140 pages… Why would length matter?
Length matters because the tool actually isn’t very good at analyzing text in the manner that Twitter and Facebook researchers have tasked it with. When you ask it to analyze positive or negative sentiment of a text, it simply counts negative and positive words within the text under study. For an article, essay or blog entry, this is fine — it’s going to give you a pretty accurate overall summary analysis of the article since most articles are more than 400 or 500 words long.
For 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.”
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).
That’s a huge difference if you’re interested in unbiased and accurate data collection and analysis.
And since much of human communication includes subtleties such as this — without even delving into sarcasm, short-hand abbreviations that act as negation words, phrases that negate the previous sentence, emojis, etc. — you can’t even tell how accurate or inaccurate the resulting analysis by these researchers is. Since the LIWC 2007 ignores these subtle realities of informal human communication, so do the researchers.2
Perhaps it’s because the researchers have no idea how bad the problem actually is. Because they’re simply sending all this “big data” into the language analysis engine, without actually understanding how the analysis engine is flawed. Is it 10 percent of all tweets that include a negation word? Or 50 percent? Researchers couldn’t tell you.3
Even if True, Research Shows Tiny Real World Effects
Which is why I have to say that even if you believe this research at face value despite this huge methodological problem, you’re still left with research showing ridiculously small correlations that have little to no meaning to ordinary users.
For instance, 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. The researchers themselves acknowledge as much, noting that their effect sizes were “small (as small as d = 0.001).” READ MORE…