To Tango or to Disentangle? Making Ethnography Public in the Digital Age
- URL: http://arxiv.org/abs/2602.08349v1
- Date: Mon, 09 Feb 2026 07:32:56 GMT
- Title: To Tango or to Disentangle? Making Ethnography Public in the Digital Age
- Authors: Daniel Mwesigwa, Cyan DeVeaux, Palashi Vaghela,
- Abstract summary: We argue that the rise of digital platforms has introduced new opportunities as well as practical and ethical challenges.<n>We examine how ethnographers employ diverse tactics to study both enduring and emerging socio-cultural issues of race and caste.<n>We propose emergent relationality as a key analytic for understanding the mutual shaping of ethnographers, platforms, and publics.
- Score: 6.777769184482646
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ethnography attends to relations among people, practices, and the technologies that mediate them. Central to this method is the duality of roles ethnographers navigate as researchers and participants and as outsiders and insiders. However, the rise of digital platforms has introduced new opportunities as well as practical and ethical challenges that reshape these dualities across hybrid media environments spanning both online and offline contexts. Drawing on two case studies of VRChat and WhatsApp, we examine how ethnographers employ diverse tactics to study both enduring and emerging socio-cultural issues of race and caste, particularly those that form what are often called publics. We propose emergent relationality as a key analytic for understanding the mutual shaping of ethnographers, platforms, and publics. In this work, emergent relationality offers registers for analyzing how positionality and hybrid media environments constitute and condition what can be accessed, articulated, and made public.
Related papers
- The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook [62.2627874717318]
Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies.<n>Using a public API snapshot collected about five days after launch, we address three research questions: what AI agents discuss, how they post, and how they interact.<n>We show that agents' writing is predominantly neutral, with positivity appearing in community engagement and assistance-oriented content.
arXiv Detail & Related papers (2026-02-13T05:28:31Z) - A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
stance detection has emerged as a crucial subfield within affective computing.
Recent years have seen a surge of research interest in developing effective stance detection methods.
This paper provides a comprehensive survey of stance detection techniques on social media.
arXiv Detail & Related papers (2024-09-24T03:06:25Z) - Ethos and Pathos in Online Group Discussions: Corpora for Polarisation Issues in Social Media [6.530320465510631]
Growing polarisation in society caught the attention of the scientific community as well as news media.
We propose to approach the problem by investigating rhetorical strategies employed by individuals in polarising discussions online.
We develop multi-topic and multi-platform corpora with manual annotation of appeals to ethos and pathos, two modes of persuasion in Aristotelian rhetoric.
arXiv Detail & Related papers (2024-04-07T09:10:47Z) - Understanding Hybrid Spaces: Designing a Spacetime Model to Represent
Dynamic Topologies of Hybrid Spaces [0.0]
The paper develops atemporal model for the visualization of dynamic topologies of hybrid spaces.
Existing concepts and types of representation of hybrid spaces are presented.
Various dynamic topologies of hybrid spaces were successfully visualized.
arXiv Detail & Related papers (2024-03-08T11:18:27Z) - Modeling Political Orientation of Social Media Posts: An Extended
Analysis [0.0]
Developing machine learning models to characterize political polarization on online social media presents significant challenges.
These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data.
We introduce two methods that leverage on news media bias and post content to label social media posts.
We demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts.
arXiv Detail & Related papers (2023-11-21T03:34:20Z) - Getting aligned on representational alignment [93.08284685325674]
We study the study of representational alignment in cognitive science, neuroscience, and machine learning.
Despite their overlapping interests, there is limited knowledge transfer between these fields.
We propose a unifying framework that can serve as a common language for research on representational alignment.
arXiv Detail & Related papers (2023-10-18T17:47:58Z) - Intersectional Inquiry, on the Ground and in the Algorithm [1.0923877073891446]
We argue that methods in this field must account for intersections of social difference, such as race, class, ethnicity, culture, and disability.
We consider the complexities of bringing together computational and qualitative methods in an intersectional methodological approach.
arXiv Detail & Related papers (2023-08-29T23:43:58Z) - The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Fairness meets Cross-Domain Learning: a new perspective on Models and
Metrics [80.07271410743806]
We study the relationship between cross-domain learning (CD) and model fairness.
We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks.
Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
arXiv Detail & Related papers (2023-03-25T09:34:05Z) - Self-supervised Hypergraph Representation Learning for Sociological
Analysis [52.514283292498405]
We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria.
First, we propose an effective hypergraph awareness and a fast line graph construction framework.
Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
arXiv Detail & Related papers (2022-12-22T01:20:29Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.