Lens Profile Insights
Diving Deep to discover insights around the relationship of activities and interactions in the Lens social graph
Last updated
Diving Deep to discover insights around the relationship of activities and interactions in the Lens social graph
Last updated
EigenTrust algorithms powered by OpenRank is used to better understand social interactions within the Lens Protocol community. By using the engagement strategies to rank Lens users (we'll refer them as profiles from now on), we now have surfaced insights in a visual time series dashboard as well as scatter graphs.
How does a profile's outwardly actions affect how well the are engaged inwardly? When Lens profiles initiate actions such as following other profiles, authoring a new posts, commenting on other posts, liking posts/comments and even like (upvoting) other content, we found that it really depends on the reputation on the author that these Lens profiles are acting towards. This inherently infers the quality of those posts/comments they interacted with to be noteworthy or not in a general community's point of view. By analyzing these actions, we aim to understand a profile's contribution to the entire Lens protocol community.
We also looked at how many types of interactions a Lens profile receives, across all types of interactions on their profile and content — whether they are followed by others, or their posts are mirrored, liked or commented by others. The inherent reputation of other profiles interacting with a profile of interest, is what we've surfaced in this most recent iteration in this visualization.
How about the relationships between one profile with another? If you'd like to discover how posts and comments are interacted upon by a single profile with another, we have that here as well, plotted over time
We first classified actions and interactions for each profile. Then, aggregated each type's follows, post/comment likes, post/comment mirrors and divided them with the population's average totals of the same type, within actions and interactions.
By analyzing these in three cohorts ranked by EigenTrust, we were able to visualize* which profiles post quality content, like interesting posts, comment on popular threads (and reciprocated by others), and how potentially sybil profiles and spammers try really hard to farm engagement. EigenTrust computed trust rankings are able to gauge the credibility and influence of profiles in Lens Protocol.
We visualized each cohort by placing them on a chart with the X-axis showing outward actions with the Y-axis as the amount of inbound interactions, on a non-linear logarithmic scale, we're able to draw the line between at the 0 scale. If they fall below average, say their post doesn't get liked as much as the average population, then the value computed for this type, when dividing with the average totals, will be less than 1.0. The log function of a number less than 1.0, will be will be a negative number (e.g. log(0.1) = -1). If they are above average in a type of interaction, say get liked a lot more for each post, then they'll be in the positive range
