Can Online Systems Discern Truth and Beauty or All That One Needs to Know?
October 14, 2015
Last week I fielded a question about online systems’ ability to discern loaded or untruthful statements in a plain text document. I responded that software is not yet very good at figuring out whether a specific statement is accurate, factual, right, or correct. Google pokes at the problem in a number of ways; for example, assigning a credibility score to a known person. The higher the score, the person may be more likely to be “correct.” I am simplifying, but you get the idea: Recycling a variant of Page Rank and the CLEVER method associated with Jon Kleinberg.
There are other approaches as well, and some of them—dare I suggest, most of them—use word lists. The idea is pretty simple. Create a list of words which have positive or negative connotations. To get fancy, you can work a variation on the brute force Ask Jeeves’ method; that is, cook up answers or statement of facts “known” to be spot on. The idea is to match the input text with the information in these word lists. If you want to get fancy, call these lists and compilations “knowledgebases.” I prefer lists. Humans have to help create the lists. Humans have to maintain the lists. Get the lists wrong, and the scoring system will be off base.
There is quite a bit of academic chatter about ways to make software smart. A recent example is “Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network.” In the conclusion to the paper, which includes lots of fancy math, I noticed that the researchers identified the foundation of their approach:
This paper studied the sentiment diffusion of online public opinions about hot events. We adopted the dictionary-based sentiment analysis approach to obtain the sentiment orientation of posts. Based on HowNet and semantic similarity, we calculated each post’s sentiment value and classified those posts into five types of sentiment orientations.
There you go. Word lists.
My point is that it is pretty easy to spot a hostile customer support letter. Just write a script that looks for words appearing on the “nasty list”; for example, consumer protection violation, fraud, sue, etc. There are other signals as well; for example, capital letters, exclamation points, underlined words, etc.
The point is that distorted, shaped, weaponized, and just plain bonkers information can be generated. This information can be gussied up in a news release, posted on a Facebook page, or sent out via Twitter before the outfit reinvents itself.
The researcher, the “real” journalist, or the hapless seventh grader writing a report will be none the wiser unless big time research is embraced. For now, what can be indexed is presented as if the information were spot on.
How do you feel about that? That’s a sentiment question, gentle reader.
Stephen E Arnold, October 14, 2015
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