Social Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious
Design Choices
- URL: http://arxiv.org/abs/2304.14679v1
- Date: Fri, 28 Apr 2023 08:12:38 GMT
- Title: Social Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious
Design Choices
- Authors: Marc Cheong
- Abstract summary: Social media has expanded in its use, and reach, since the inception of early social networks in the early 2000s.
We argue that as information (eco)systems, social media sites are vulnerable from three aspects.
We will unpack suggestions from various allied disciplines in untangling the 3A's above.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social media has expanded in its use, and reach, since the inception of early
social networks in the early 2000s. Increasingly, users turn to social media
for keeping up to date with current affairs and information. However, social
media is increasingly used to promote disinformation and cause harm. In this
contribution, we argue that as information (eco)systems, social media sites are
vulnerable from three aspects, each corresponding to the classical 3-tier
architecture in information systems: asymmetric networks (data tier);
algorithms powering the supposed personalisation for the user experience
(application tier); and adverse or audacious design of the user experience and
overall information ecosystem (presentation tier) - which can be summarized as
the 3 A's. Thus, the open question remains: how can we 'fix' social media? We
will unpack suggestions from various allied disciplines - from philosophy to
data ethics to social psychology - in untangling the 3A's above.
Related papers
- Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias [64.73474454254105]
Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users.
Existing social recommendation models fail to address the issues of popularity bias and the redundancy of social information.
We propose a Condition-Guided Social Recommendation Model (named CGSoRec) to mitigate the model's popularity bias.
arXiv Detail & Related papers (2024-05-27T02:45:01Z) - Yourfeed: Towards open science and interoperable systems for social
media [1.8623205938004257]
Existing social media platforms make it incredibly difficult for researchers to conduct studies on social media.
To close the gap, we introduce Yourfeed, a research tool for conducting ecologically valid social media research.
arXiv Detail & Related papers (2022-07-15T13:49:51Z) - Adherence to Misinformation on Social Media Through Socio-Cognitive and
Group-Based Processes [79.79659145328856]
We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation.
We make the case that polarization and misinformation adherence are closely tied.
arXiv Detail & Related papers (2022-06-30T12:34:24Z) - Detecting Ideal Instagram Influencer Using Social Network Analysis [0.0]
The paper focuses on social network analysis (SNA) for a real-world online marketing strategy.
The study contributes by comparing various centrality measures to identify the most central nodes in the network and uses a linear threshold model to understand the spreading behaviour of individual users.
arXiv Detail & Related papers (2021-07-12T20:53:58Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - Analysing Social Media Network Data with R: Semi-Automated Screening of
Users, Comments and Communication Patterns [0.0]
Communication on social media platforms is increasingly widespread across societies.
Fake news, hate speech and radicalizing elements are part of this modern form of communication.
A basic understanding of these mechanisms and communication patterns could help to counteract negative forms of communication.
arXiv Detail & Related papers (2020-11-26T14:52:01Z) - Named Entity Recognition for Social Media Texts with Semantic
Augmentation [70.44281443975554]
Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts.
We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
arXiv Detail & Related papers (2020-10-29T10:06:46Z) - Study of the usability of LinkedIn: a social media platform meant to
connect employers and employees [91.3755431537592]
This paper is assessing LinkedIn's usability using both user and expert evaluation.
The overall usability of LinkedIn application has been measured by using SUS (System Usability Scale)
arXiv Detail & Related papers (2020-06-06T18:19:45Z) - Exposure to Social Engagement Metrics Increases Vulnerability to
Misinformation [12.737240668157424]
We find that exposure to social engagement signals increases the vulnerability of users to misinformation.
To reduce the spread of misinformation, we call for technology platforms to rethink the display of social engagement metrics.
arXiv Detail & Related papers (2020-05-10T14:55:50Z) - SocialTrans: A Deep Sequential Model with Social Information for
Web-Scale Recommendation Systems [29.24459965940855]
We present a novel deep learning model SocialTrans for social recommendations.
The first module is based on a multi-layer Transformer to model users' personal preference.
The second module is a multi-layer graph attention neural network (GAT), which is used to model the social influence strengths between friends in social networks.
The last module merges users' personal preference and socially influenced preference to produce recommendations.
arXiv Detail & Related papers (2020-05-09T03:39:45Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
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.