How are Conversations via an On-Demand Peer-To-Peer Emotional Well-Being App Associated with Emotional Improvement?

Figures & data

Figure 1. Graphical Representation of the Model to Examine Emotional Synchrony.

Note. The figure is a graphical representation of the multilevel model used to examine hypotheses H3a and H3b (Equations 2(2) $\mathit{ListenerSentimen}{t}_{\mathit{dt}}={\beta }_{0\mathit{d}}+{\beta }_{1\mathit{d}}\mathit{StateUserSentimen}{t}_{\mathit{dt}}+{\beta }_{2\mathit{d}}\mathit{TurnPai}{r}_{\mathit{dt}}+e_{\mathit{dt}}$(2) –5(5) ${\beta }_{2\mathit{d}}={\gamma }_{20}$(5) ). The rectangles represent manifest variables, the circles represent latent variables, the two headed arrows represent variances and covariances, the one-headed arrows represent regressions, and where the triangles represent vectors of 1 that invoke intercepts. In this graphical representation of the model that was fit to the observed data, the intercepts (β_0d) and slopes (β_1d) of the within-dyad relations, where listeners’ time-varying emotion sentiment are regressed on users’ time-varying sentiment, are shown as small circles on their associated arrows (i.e., the random effects). These parameters are then projected and summarized as Overall Listener Emotion Sentiment and Emotional Synchrony factors that are then regressed at the between-dyad level on the Trait User Sentiment and Affect Change variables and their interaction. The relations of interest for testing H3a (γ₁₀, the prototypical level of emotional synchrony) and H3b (γ10 commonly used emojis we₁₂, the relation between differences in affect change and emotional synchrony) are shown as bold lines.

Figure 2. Change in Users’ Affective State from Pre- to Post Conversation.

Note. Users’ pre- and post-conversation affective state is plotted along the x-axis, with lines connecting individual user’s reports. Each small dot represents an individual’s report of affective state, with the dots (i.e., affective state reports) being jittered slightly to reduce the overlap of points and facilitate visibility. The lines connecting pre- and post-conversation affective state represent change in affective state, with thicker lines indicating where more individuals followed a particular trajectory.

Table 1. Results from regression examining change in users’ affective state.

	Primary Analysis			Replication Analysis
	Estimate	SE	β	Estimate	SE	β
Intercept	1.09*	0.04		0.98*	0.04
Pre-conversation affective state (centered)	−0.72*	0.05	−0.60*	−0.78*	0.04	−0.62*
R^2	0.36			0.38
F	225.20*			369.20*

N = 402 users for primary sample and N = 608 for replication sample. Estimate = unstandardized estimate. SE = standard error. β = standardized estimate. * = p < . 05.

Figure 3. Results from Regression Model Predicting Affective State Change from Language Style Matching.

Note. The figure depicts the association between language style matching and affective state change (H2b).

Table 2. Results from regression examining the association between lsm and change in users’ affective state.

	Primary Analysis			Replication Analysis
	Estimate	SE	β	Estimate	SE	β
Intercept	−1.11*	0.49		−0.78*	0.24
Language Style Matching (LSM)	2.55*	0.61	0.17*	2.05*	0.32	0.21*
Pre-conversation affective state (centered)	−0.73*	0.05	−0.60*	−0.77*	0.04	−0.60*
Total turns	0.00	0.00	0.07	0.00*	0.00	0.12*
R^2	0.40			0.46
F	88.87*			170.50*

N = 402 users for primary sample and N = 608 for replication sample Estimate = unstandardized estimate. SE = standard error. β = standardized estimate. * = p < . 05.

Table 3. Results from multilevel model examining the association between users’ and listeners’ emotional sentiment and change in users’ affective state.

	Estimate	SE
Fixed Effects
Intercept, ${\gamma }_{00}$	0.66*	0.00
Turn pair (proxy for time), ${\gamma }_{20}$	0.00*	0.00
Trait user sentiment, ${\gamma }_{01}$	0.47*	0.06
State user sentiment, ${\gamma }_{10}$	0.12*	0.02
Affective state change, ${\gamma }_{02}$	−0.01*	0.00
Trait * state user sentiment, ${\gamma }_{11}$	0.17	0.33
Affective state change * state user sentiment, ${\gamma }_{12}$	0.02	0.02
Affective state change * trait user sentiment, ${\gamma }_{03}$	−0.06	0.05
Random Effects	SD	Corr
Intercept, $u_{0\mathit{d}}$	0.03
State user sentiment, $u_{1\mathit{d}}$	0.16	0.12
Residual, $e_{\mathit{dt}}$	0.12

N = 402 users (6,252 observations – i.e., turn pairs). Estimate = unstandardized estimate. SE = standard error. SD = standard deviation. Corr = correlation.

*= p < . 05.