Social Group and Social Media Tethering
If you are a social media influencer, social media algorithms are frustrating due to “the algorithm.” By algorithm, they mean the machine learning code that changes occasionally through constant testing and optimization. I’ve weighed and observed them since I first started blogging. The yin-yang interplay is due to a concept I’ve termed as tethering–both through machine learning and our own social groups. Reflecting on that, I’d like to share a few relevant observations.
Social Media Tethering
Social media tethering is the linkage strength an algorithm attributes to two specific individual accounts. The more interactions the two accounts have, the stronger the link which can come at the cost of the linkage strength of other respective accounts. In this way, the algorithm begins to create “nodes” of stronger or weaker connections between people and among groups of people. A few interactions that can strengthen linkage include:
- Viewing an account
- Sending a direct message
- Commenting on, liking, or sharing a post
- Spending time viewing a post
- Tagging an account
- Cross-platform activity and/or connections
Social media tethers can be strong or weak. I mentioned that I had studied social media, and I’d like to refer you to a one-year 2017 study I conducted on Instagram. While this is one of my favorite studies ever, I can direct you to the 12:30 mark of the video that starts to discuss the Instagram feed for roughly 10 minutes. If ever there is an indication of social media tethering, the feed is that indication. Who pops? Who drops? It’s all dependent on social media tethering. Take a look, and feel free to poke fun at my very YouTube video thumbnail, one of the most beautiful photos in the history of me. (If you like the video, please feel free to watch the rest as well! While slightly outdated, the principles still likely apply.)
Not to be outdone, I also studied the impact of posting on LinkedIn for six months this year. While I posted as a New Year’s Resolution, I also thought that I might as well study what transpired. Without getting into many details, LinkedIn’s algorithm determined that my posts were not interesting to my network. Consequently, the number of views my posts received over time decreased from roughly 1000 to roughly 50 per post across six months. At the nadir, a single post that was designed to support someone else reached only 20 individuals. Even on my most recent posts, there was a lingering tethering effect.
Machine learning is impressive!
Social Group Tethering
That being said, machine learning would be nothing without the true social network–our lives. We have family, friends, coworkers, business connections, and more. As we interact with people offline, we build tethers as well. It’s social group tethering in an offline world through relationship interactions that cause interpersonal bonds to grow.
Just like online social networks, our offline tethers increase the likelihood that we’ll spend more time with those tighter-tethered individuals and less time with those looser-tethered individuals.
And that offline dynamic can certainly impact the online dynamic.
Conclusions
Baby photos, celebrations of birthdays, promotions, and all kinds of other life events are celebrated with high-fives, hugs, and smiles in person. Seen online, photos, text, and videos of a similar nature prompt a multitude of likes, comments, upvotes, and love.
Both interaction sets build social tethers.
Regardless of where the social tethering happens (and in what magnitude), the impact on social media’s underlying machine learning architecture is apparent. Marketers can take a lesson from this dynamic by striving to build relationship in both online and offline worlds to drive strength in the social media tethering process.
And people can take a lesson from this dynamic by exercising self-awareness in the tethering process.
