Exploring the Boundaries of Ambient Awareness in Twitter

TL;DR

Ambient awareness on Twitter is investigated through in-wall ambient awareness (IWAA), a wall-based manifestation of knowledge about who-knows-what. The authors adopt a two-step, data-driven approach: first, they examine explicit awareness signals via reactions to experts’ content; second, they quantify passive exposure through a bounded, wall-based visibility model that accounts for online presence. Using a large Twitter dataset of Lists, seekers, and experts, they find that while a subset of seekers shows reactions to experts, the majority are passive; furthermore, many seekers experience minimal or zero exposure to experts’ content in their walls, especially when not following the experts. The results suggest that limited exposure to diagnostic tweets—rather than limited cognitive capacity—restricts IWAA, and they provide a methodology to study ambient awareness at scale with potential extensions to other topics and platforms.

Abstract

Ambient awareness refers to the ability of social media users to obtain knowledge about who knows what (i.e., users' expertise) in their network, by simply being exposed to other users' content (e.g, tweets on Twitter). Previous work, based on user surveys, reveals that individuals self-report ambient awareness only for parts of their networks. However, it is unclear whether it is their limited cognitive capacity or the limited exposure to diagnostic tweets (i.e., online content) that prevents people from developing ambient awareness for their complete network. In this work, we focus on in-wall ambient awareness (IWAA) in Twitter and conduct a two-step data-driven analysis, that allows us to explore to which extent IWAA is likely, or even possible. First, we rely on reactions (e.g., likes), as strong evidence of users being aware of experts in Twitter. Unfortunately, such strong evidence can be only measured for active users, which represent the minority in the network. Thus to study the boundaries of IWAA to a larger extent, in the second part of our analysis, we instead focus on the passive exposure to content generated by other users -- which we refer to as in-wall visibility. This analysis shows that (in line with \citet{levordashka2016ambient}) only for a subset of users IWAA is plausible, while for the majority it is unlikely, if even possible, to develop IWAA. We hope that our methodology paves the way for the emergence of data-driven approaches for the study of ambient awareness.

Figures (12)

• Figure 1: Topics considered in our analysis and number of Lists collected for each of them.
• Figure 2: We show the ICDF for different features of the seekers (blue) and the experts (orange). Specifically, we show the rate of posts $R$ (separated by tweets, retweets and answers), the count of likes $A_l$, friends $B$, and followers $F$.
• Figure 3: ICDF for the four average reaction signals, we generally represent with $I$.
• Figure 4: Clustering results. (a) Size proportion of each cluster. (b) Average interaction signals per cluster.
• Figure 5: ICDF of experts' features per cluster.