Andrew Yang (Reddit)

Reddit is getting more and more popular these days; its vision of becoming “the front page of the internet” looks encouraging. As a user, I see Reddit as an aggregation of real people’s opinions. This makes Reddit data very promising for garnering insights.

Throughout the election season, I want to know what people are actually saying about a candidate, for example, Andrew Yang. You might be surprised to learn just how much data there is on Reddit about him. Addy, our qualitative research tool, found 8,864 user comments from dozens of subreddits related to Andrew Yang in less than a month’s timeframe (Sep 18-Oct 10). While harvesting such a gold mine of comments for insights holds great promise,
there are some obvious difficulties in doing so. First of all, it would be very difficult for people to manually consume such a large amount of text data. Additionally, people don’t comment in an organized manner, rather they mix topics throughout the various threads. Therefore, it is difficult to find patterns such as what are the leading topics and what are people’s sentiment for each of those topics.

What can help overcome these barriers for harvesting Reddit data for insights is an AI assistant, like Addy, that can produce a qualitative summary of what would be otherwise non-consumable text data.
In the previously mentioned Andrew Yang dataset, Addy brokedown the 8,864 comments into several interesting topics. One topic read “reduce the number”. This topic is about Yang supporters convincing an anti-abortionist that universal basic income would help “reduce the number” of abortions. (See I am anti-abortion, and I'm torn between voting for Andrew Yang and not voting.)

In another topic, users are fiercely debating whether “universal basic income” is “anti-capitalist”. Other interesting topics include “the right to be informed” as digital data is your personal property, “Rent control” under universal basic income, how to fund “Freedom Dividend” and so forth.

I was excited by the novel talking points and vocabulary I was able to uncover so easily with Addy’s help. A lot of times I also use Addy as “smart” filters on text data so I can quickly look at the context of key phrases, people or strong emotions and then capture my Cliff notes with Addy’s workbench.




For example, when following the “Angry” emotion under “Freedom Dividend”, I found strong emotions around “adding tax” to fund UBI. An example of this emotion would have taken a lot of time to find without the Addy tool. (See image to the right)

I encourage you to try Addy as your AI assistant for consuming text data and for coming up with your own unique insights. In the era of big data, humans are ironically more ignorant, isolated and misinformed than ever before. To rise above all of the noise, we should leverage AI to do our own research.
 
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