Building an ICCN Multimodal Classifier of Aggressive Political Debate Style: Towards a Computational Understanding of Candidate Performance Over Time

Shah, Dhavan V.; Sun, Zhan-Li; Bucy, Erik P.; Kim, Sang Jeong; Sun, Yuangong; Li, Mengyu; Sethares, William A.

doi:10.1080/19312458.2023.2227093

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…Some studies have explored social network analyses to model reply behavior in the workplace [9,10]. On the other hand, fewer papers have explored building multimodal classifiers for text classification [13]. Building on prior work, in this study, we propose EMMA, an Email MultiModal Architecture, which trains deep learning models to predict whether or not an email will receive a reply in terms of the linguistic, organizational, and interpersonal context it embeds.…”

Section: Research Objectivesmentioning

confidence: 99%

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Shah,

Jaidka,

Ungar

et al. 2023

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Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender’s social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender’s organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories.

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Section: Research Objectivesmentioning

confidence: 99%

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Shah,

Jaidka,

Ungar

et al. 2023

Information

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Automated Visual Analysis for the Study of Social Media Effects: Opportunities, Approaches, and Challenges

Peng,

Lock,

Ali Salah

2023

Communication Methods and Measures

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To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and the theoretical concepts relevant to media effects research. We propose multiple approaches to bridging this gap in automated visual analysis, such as justifying the theoretical significance of specific visual features in existing tools, developing supervised learning models to measure a visual attribute of interest, and applying unsupervised learning to discover meaningful visual themes and categories. We conclude with a discussion about future directions for automated visual analysis in computational communication research, such as the development of benchmark datasets designed to reflect more theoretically meaningful concepts and the incorporation of large language models and multimodal channels to extract insights.

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Audio-as-Data Tools: Replicating Computational Data Processing

Lukito,

Greenfield,

Yang

et al. 2024

MaC

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The rise of audio-as-data in social science research accentuates a fundamental challenge: establishing reproducible and reliable methodologies to guide this emerging area of study. In this study, we focus on the reproducibility of audio-as-data preparation methods in computational communication research and evaluate the accuracy of popular audio-as-data tools. We analyze automated transcription and computational phonology tools applied to 200 episodes of conservative talk shows hosted by Rush Limbaugh and Alex Jones. Our findings reveal that the tools we tested are highly accurate. However, despite different transcription and audio signal processing tools yield similar results, subtle yet significant variations could impact the findings’ reproducibility. Specifically, we find that discrepancies in automated transcriptions and auditory features such as pitch and intensity underscore the need for meticulous reproduction of data preparation procedures. These insights into the variability introduced by different tools stress the importance of detailed methodological reporting and consistent processing techniques to ensure the replicability of research outcomes. Our study contributes to the broader discourse on replicability and reproducibility by highlighting the nuances of audio data preparation and advocating for more transparent and standardized practices in this area.

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Building an ICCN Multimodal Classifier of Aggressive Political Debate Style: Towards a Computational Understanding of Candidate Performance Over Time

Cited by 4 publications

References 43 publications

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Automated Visual Analysis for the Study of Social Media Effects: Opportunities, Approaches, and Challenges

Audio-as-Data Tools: Replicating Computational Data Processing

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