The Stories Behind the Stats: What Social Media Data Can (and Can't) Tell Us (Fowler, Week 9 )

 


Picture this: it’s 2 a.m., you’re deep in a doomscroll of climate issues, foreign wars, conspiracy theories, and the intermittent AI ASMR video that always appears, and somehow, this counts as research. Welcome, one and all, to the world of social media analytics, where every post, tweet, and hashtag becomes data. This data tells stories about how you connect, learn, and sometimes…completely lose the plot.

This week we broke down the big picture of social media research, that being how platforms give us mountains of user-generated data that can reveal real-world patterns. But, those patterns are only part of the story.

Wang and Ye (2018)’s article describes how social media helped disaster managers visualize floods, fires, and community distress in real time. Tweets acted like digital distress flares, mapping where people needed help, which areas were most impacted, and how communities were responding. With geotags, keywords, and timestamps, emergency responders could track crises faster than traditional reporting ever allowed, showing a powerful example of how social media data is doing tangible good.

But, as we know, teamwork makes the dream work—data cannot function alone, as it’s biased and filled with gaps. In an article about the #FilmYourHospital conspiracy, a single tweet suggesting COVID-19 was a hoax quickly went viral after being amplified by notable public figures (looking at you, politicians). Authors Gruzd and Mai found that the spread of misinformation was fueled by real users, not bots. Influencers, politicians, and even every day users looking for a few extra likes or their next big break amplified the message because it fit their beliefs or stirred strong emotions. That’s the crazy new reality of virality: misinformation moves fastest when it feels personal.

For libraries, this all of course circles back to our role as both information stewards and digital citizens. The same tools used to track wildfires or viral conspiracies could help information professionals understand their community—what they’re asking, sharing, and worrying about online. Social media analytics can inform programming, outreach, and even crisis communication, but only if approached with care. Data might show trends, but it’s librarians who interpret them with empathy and context.

How do you think libraries can use social media analytics responsibly, without crossing the line into surveillance or losing that human touch?


--Lauren Fowler


Comments

  1. Hi Lauren!
    I really enjoyed your take on this week's readings! I actually read these two readings as well and, while my views tend to align with the authors of the article of the #FilmYourHospital conspiracy, it struck me how much of their political bias actually made it into the finished product. I get that these scholars do not have the burden of necessarily reporting both sides of the issue, and I absolutely believe that these conspiracies were driven by those politicians, but I wonder if there were other large accounts that drove the virality of the hashtag that didn't necessarily fit the narrative they ended up building? Misinformation is not always politically one sided. Sometimes there is misinformation on both, or all sides. And I think that is what makes it so much harder for us as information professionals, to weed through all of the negative and bashing and search for the truth behind the curtain. I think in this day and age of politics, the inclination to deceive instead of lead with the truth is what leads to the most divide. Because no one knows what to believe anymore and we all know that the truth comes out eventually.

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  2. I also enjoyed Lauren's take on this week's readings (Thank you Lauren!) and I would like to add to Beth's comments. It is hard to research all the places where a particular hashtag was used, especially when the hashtag becomes banned on any of the social media sites. I am not making a judgment call about any hashtag banns. I am merely pointing out that it is difficult to actually know at this point all the social media accounts that used the hashtag (even though tweets are supposed to be preserved) because it was used on so many different platforms before it was removed. As we also saw in our readings, the sheer volume of information that is transferred through social media sites can make it a challenge to find direct sources of particular messaging.

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