'Resourcefulness creating Expansion: This Twitter Journey is dominated by people joining the conversation. Increased discussion 'Fullness' (~engagement) is great! But maintaining CommunityFitness with lots of newcomers can be a challenge as they may not appreciate the nuances that make the existing community unique'
Experimental Analysis by ThenAtlas.org
solely for informational purposes – see disclaimer:
Beyond grabbing attention – creating a strong sense of community is truly valuable – but how can this be accomplished? Step 1, measure current community strength – this is our specialty, then Step 2, identify where it comes from – this is where we need your help. The definition of “strength” varies from trust, to enthusiasm and even to love – as community members, you are part of the community, thus you are the experts.
Animation 1. Shows recent change in community metrics. Time goes left to right -> most recent. CommunityFitness metric (thick gold line) and Confidence Interval (pale yellow area) show the level & possible range of vibrancy, respectively.
In the animation, CommunityFitness moving up is more vibrant, down is less vibrant eventually bottoming out at: Baseline, where the community lacks observable fabric and interactions are essentially random. Below baseline, in-transition suggests a community fabric that is changing direction.
Fullness (thick blue line) is the active community size that can move up (larger) or down (smaller). This metric is analogous to average rate of engagement.
Change associated with the:
Last 200 (or 50, as noted) tweets are highlighted with arrows and %change labels.
Comparison to other public figures (labels on right side of Animation 1):
A. Stephanie Schriock (@Schriock1, Washington, DC) Proud Montanan and Democrat; President of EMILY's List.
B. Leah Vukmir (@LeahVukmir, Brookfield, WI) Mom. Nurse. Fighter. Running for Senate to bring the Wisconsin Way to DC.
C. IBEW (@IBEW) Official Twitter account of the International Brotherhood of Electrical Workers (IBEW) – the largest union of electrical workers in North America
D. Mike Pence (@mike_pence) Vice President of the United States
How to increase Vibrance!?
One central theme that emerges is strong community has appreciation for those who inspire through unique value. @Kohls literally gives away $, @packers try to win, @DaliLama provides deep knowledge, @JohnMcCain shared his spirit, rest in peace. In contrast, news blasts, clickbait and repetitive messaging are associated with reduced vibrance. To figure out what makes a Twitter community vibrant:
- Consider how other relevant tweeters (A-D above, if available) create community.
- Compare vibrant vs in transition tweets (below) – is there a difference?
- Use the Journey Mapping (below) to identify trajectories that need exploration!
- Ask us for further consultation email@example.com or via the comments section (below).
Individual tweet analysis
While it is impossible to pin CommunityFitness on a single tweet, some tweets seem to have a larger influence than others – thus it can be informative to identify characteristics of tweets associated with change. Note changes in CommunityFitness can also come from external sources (e.g. news coverage), in which case the association with a single tweet is coincidental. Also please note, “in transition” is not “bad” – it can be the start of a vibrant trajectory.
Vibrant example tweets
In-transition example tweets
Are there any characteristics of these tweets that might influence CommunityFitness? Share your insight in the comments, below!
“Order from Chaos” – Journey Mapping
Technically, building CommunityFitness requires a web of interactions covering six primary characteristics: Resistance, Resilience, Revolution, Expansion, Disruption, Contraction. Vibrance is defined as patterns of change within a community’s complex conversational journey through these characteristics. See example tweets and #hashtags as guides for what previously defined each trajectory. The same #hashtag across trajectories indicates changing value through time, or that it is used frequently.
Journey Mapping with Example Tweets & #Hashtags
Resistance trajectory not well represented
How is this possible? About these analyses:
These independent analyses are an offshoot of the global 2018 Twitter Health Metric Proposal process.
The results presented herein were derived from Evidence-based Tradeoff Mapping (ETM), a holistic new framework that uses the balance of competing elements within complex systems to identify patterns of change along a continuum of possible trajectories to quantify CommunityFitness and correlated factors. The ETM framework was derived from research in forest systems (dynamics of ~1 billion trees) and the exact methodology is maintained as a trade secret. Secrecy is needed for the same reason Google’s search algorithm is secret. And like Google, validation is in the results. Let us know what is good (and bad) about these results in the comments below.
Why? From the founder:
There is no more simple yet beautifully powerful medium of global conversation than Twitter: humanity in a sentence or two, iterated a half-billion times per day. But is humanity coming closer together or splitting apart? Why? Who will win?
It is within our reach to answer very very big questions about life. We are starting at the level of a Tweeter’s community and if successful, we will be scaling up!
Please note ThenAtlas.org has no financial affiliation with Twitter Inc., we simply want to make the world a more understood place.
-TD Andrews, PhD
If these analyses have any value to you – please let us know in the comments as well as though support: ThenAtlas.org/support
Request a Twitter community analysis:
All content on this site is for informational purposes only. Analyzing tweets is a common practice described here. However, if you feel we have violated ownership rights or misrepresented your associated Twitter community, please let us know immediately: firstname.lastname@example.org.
Please see the full disclaimer at: ThenAtlas.org/terms-conditions