This paper analyzes functionality of social media in social movements through a case study of Tik Tok content regarding the Black Lives Matter Movement. A mixed methods approach was used: surveys of Tik Tok users and a content analysis of Tik Toks. Results show support for existing literature about echo chambers: people tend to mostly see videos they agree with and are not likely to change their opinions based off of Tik Toks they see. Tik Toks about the Black Lives Matter movement were often intended to document protests and police brutality or to engage in consciousness raising efforts.

Findings from the content analysis are included in an interactive Google Data Studios Report, embedded below.

The primary objective of this research is to understand the ways in which social media can be used in social movements. This is accomplished through an analysis of Tik Tok videos that are related to the Black Lives Matter movement.

Given the extreme popularity of Tik Tok, particularly among younger people, as well as the growing importance of social media, it is important for social movement actors to have an understanding of how to best use their platforms.

Research Question: What is the functionality of social media in social movements? How can social movement actors best use social media?

Two methods were employed: survey and content analysis.

Survey The survey was open to adults currently residing in the US. It was distributed via social media in February 2021. In total, there were 228 completed surveys.

Content Analysis A total of 200 Tik Toks were analyzed. Hashtags related to the Black Lives Matter Movement were selected and the top 25 videos under each hashtag that were able to be saved were included in analysis. Meta data on each video was tracked including: likes, views, comments, shares, themes, and functions. Additionally, if a video used an original sound, the spread of the sound was tracked.

Further Discussion

Tagfishing

It was fairly common for people to use hashtags that did not agree with the message of their video, for example conservative coded hashtags such as “bluelivesmatter” or “alm” were often used liberal leaning creators who support the Black Lives Matter movement. Oftentimes these videos are coded to look like they oppose the movement, not only through the use of hashtags but also through caption, sound, and dress. This method subverts the algorithm, allowing pro-movement messages to get on to the pages of people who may oppose the movement. Interestingly, this technique was only seen with anti-movement coded tags and not with pro-movement tags. A possible explanation is the popularity of the pro-movement tags as well as the age make-up of the app’s user base.

Bait & Switch

An interesting pattern emerged on the #alm and #bluelivesmatter tags: bait and switches. In these instances, creators coded their videos to seem as they were pro-alm/bluelivesmatter through the use of hashtags (e.g. #trump, #maga), captions, and the videos themselves. One woman started off her video saying “you may be a redneck if you know that Black Lives Matter is some racist fucking bull shit” and then switched tone completely and started explaining what systematic racism is and going into statistics about police brutality and racial bias, calling on viewers to educate themselves and support the Black Lives Matter Movement.

Boosting Videos

While the Tik Tok algorithm is constantly evolving in new and typically secretive ways, app users are fast to catch on to algorithmic changes and adapt their behavior. Creators often would call for people to “copy link share” their videos - an action they claim will boost the video on more people’s pages. There were also calls to “like, comment, share” - as these metrics were key in how much a video is boosted.

My Friend, Al Go Rhythm

There were also calls to use the comments to boost videos on people’s pages and/or fight shadow banning (when the algorithm suppresses a video/creator on the for you page). These types of comments would often use phrases like “boost” or “commenting for the algorithm.” Across the videos analyzed in the meta-analysis as well as additional videos, the method of fighting the algorithm changes over time. There are times where commenters instruct fellow viewers to use words like “algorithm” after time however, there were allegations that the algorithm had begun to suppress videos/comments that used the word “algorithm.” In response to this, people have begun using other methods to boost content including various misspellings of the word, the use of the word “boost” spelled with a combination of letters and numbers, and spamming with emojis. Other times, comments will do roll calls, asking for people’s names or favorite colors. The algorithm is constantly being updated and new allegations of suppression arise daily. Users must constantly adapt their behavior to subvert algorithmic suppression.

Echo Chambers

While not the main focus of my analysis, I did make notes regarding the comments section on videos. I generally only looked at the top five comments and then recorded if the comments were generally in agreeance with the video and how they functioned. Most of the time, comments were supportive of the video message. Additionally, in the vast majority of instances, when a new creator used a sound, they agreed with the message of the sound. Sometimes duets disagreed with sounds, but this was far less common.

An interesting pattern emerged on the #alm and #bluelivesmatter tags: bait and switches. In these instances, creators coded their videos to seem as they were pro-alm/bluelivesmatter through the use of hashtags (e.g. #trump, #maga), captions, and the videos themselves. One woman started off her video saying “you may be a redneck if you know that Black Lives Matter is some racist fucking bull shit” and then switched tone completely and started explaining what systematic racism is and going into statistics about police brutality and racial bias. This methodology helps to break out the echo chambers of the algorithm.

Reach

Numerous videos that were analyzed were reactions to posts from other social networking platforms. Typically the creator would use the “Green Screen” feature to upload a screenshot of a Tweet, Instagram, or Facebook post and then record themselves responding to it. This is unsurprising given how frequently content is shared between sites.

Discourse

Survey results and content analysis results raise some concerns about the efficiency of social media in changing public opinion and in terms of mobilizing concrete action. People tend to see videos they agree with and, when they do see a video they disagree with, are not very likely to change their opinion on a subject. This is likely a result of social media being a mediated platform that places logistical limits on discourse. Comments have a character limit and videos have a time limit; there is a limited amount of information that can be conveyed on this platform. Chanel Expansion Theory suggests that we are able to adapt our behaviors to best fit a channel.

Survey results confirm existing literature about echo chambers in social media. The content analysis also shows this with many comments sections including primarily comments in support of the message of the video. This pattern is caused by both user choice and algorithmic choice. Users tend to follow creators who make content they care about and who hold similar opinions. The algorithm is designed to keep users engaged and returning to the app and will show videos that it believes users will like. This presents a challenge for mediated movement actors: how do you break outside of the algorithmic bubble? The content analysis shows several interesting ways this can occur through the use of bait and switch techniques. Future research should examine ways social movement actors can engage in more call to action efforts and how to maximize the effectiveness of consciousness raising efforts.

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