From f57aa18e661e3519981a11d57127100e08568a67 Mon Sep 17 00:00:00 2001
From: Matt Post <post@cs.jhu.edu>
Date: Tue, 21 Jan 2025 12:52:14 -0800
Subject: [PATCH] Processed metadata corrections (closes #4408)

---
 data/xml/2024.sicon.xml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/data/xml/2024.sicon.xml b/data/xml/2024.sicon.xml
index fb54b07ade..06ad901e89 100644
--- a/data/xml/2024.sicon.xml
+++ b/data/xml/2024.sicon.xml
@@ -44,8 +44,8 @@
     </paper>
     <paper id="3">
       <title>Personality Differences Drive Conversational Dynamics: A High-Dimensional <fixed-case>NLP</fixed-case> Approach</title>
-      <author><first>Julia R.</first><last>Fisher</last><affiliation>Stanford University</affiliation></author>
-      <author><first>Nilam</first><last>Ram</last><affiliation>Stanford University</affiliation></author>
+      <author><first>Julia R.</first><last>Fischer</last></author>
+      <author><first>Nilam</first><last>Ram</last></author>
       <pages>36-45</pages>
       <abstract>This paper investigates how the topical flow of dyadic conversations emerges over time and how differences in interlocutors’ personality traits contribute to this topical flow. Leveraging text embeddings, we map the trajectories of conversations between strangers into a high-dimensional space. Using nonlinear projections and clustering, we then identify when each interlocutor enters and exits various topics. Differences in conversational flow are quantified via , a summary measure of the “spread” of topics covered during a conversation, and , a time-varying measure of the cosine similarity between interlocutors’ embeddings. Our findings suggest that interlocutors with a larger difference in the personality dimension of openness influence each other to spend more time discussing a wider range of topics and that interlocutors with a larger difference in extraversion experience a larger decrease in linguistic alignment throughout their conversation. We also examine how participants’ affect (emotion) changes from before to after a conversation, finding that a larger difference in extraversion predicts a larger difference in affect change and that a greater topic entropy predicts a larger affect increase. This work demonstrates how communication research can be advanced through the use of high-dimensional NLP methods and identifies personality difference as an important driver of social influence.</abstract>
       <url hash="e63faa80">2024.sicon-1.3</url>