Validating daily social media macroscopes of emotions
- URL: http://arxiv.org/abs/2108.07646v2
- Date: Sun, 8 May 2022 21:45:10 GMT
- Title: Validating daily social media macroscopes of emotions
- Authors: Max Pellert, Hannah Metzler, Michael Matzenberger and David Garcia
- Abstract summary: We run a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users.
We compare these with aggregated results of sentiment analysis of user discussions on the same online platform.
For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions.
- Score: 0.12656629989060433
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To study emotions at the macroscopic level, affective scientists have made
extensive use of sentiment analysis on social media text. However, this
approach can suffer from a series of methodological issues with respect to
sampling biases and measurement error. To date, it has not been validated if
social media sentiment can measure the day to day temporal dynamics of emotions
aggregated at the macro level of a whole online community. We ran a large-scale
survey at an online newspaper to gather daily self-reports of affective states
from its users and compare these with aggregated results of sentiment analysis
of user discussions on the same online platform. Additionally, we preregistered
a replication of our study using Twitter text as a macroscope of emotions for
the same community. For both platforms, we find strong correlations between
text analysis results and levels of self-reported emotions, as well as between
inter-day changes of both measurements. We further show that a combination of
supervised and unsupervised text analysis methods is the most accurate approach
to measure emotion aggregates. We illustrate the application of such social
media macroscopes when studying the association between the number of new
COVID-19 cases and emotions, showing that the strength of associations is
comparable when using survey data as when using social media data. Our findings
indicate that macro level dynamics of affective states of users of an online
platform can be tracked with social media text, complementing surveys when
self-reported data is not available or difficult to gather.
Related papers
- Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay.
This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
arXiv Detail & Related papers (2024-03-09T11:16:09Z) - Exploring Embeddings for Measuring Text Relatedness: Unveiling
Sentiments and Relationships in Online Comments [1.7230140898679147]
This paper investigates sentiment and semantic relationships among comments across various social media platforms.
It uses word embeddings to analyze components in sentences and documents.
Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.
arXiv Detail & Related papers (2023-09-15T04:57:23Z) - Machine Learning Algorithms for Depression Detection and Their
Comparison [0.0]
We have designed an automatic depression detection of online social media users by analyzing their social media behavior.
The underlying classifier is made using state-of-art technology in emotional artificial intelligence.
arXiv Detail & Related papers (2023-01-09T09:34:38Z) - 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) - Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels.
We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
arXiv Detail & Related papers (2022-11-14T18:47:26Z) - BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19
Tweets [0.7850663096185592]
The COVID-19 pandemic is one of the current events being discussed on social media platforms.
In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful.
We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models.
arXiv Detail & Related papers (2022-11-04T14:35:56Z) - 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) - Social media emotion macroscopes reflect emotional experiences in
society at large [0.12656629989060433]
Social media generate data on human behaviour at large scales and over long periods of time.
Recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level.
No research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society.
arXiv Detail & Related papers (2021-07-28T09:40:42Z) - Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia
for Human Personality Profiling [74.83957286553924]
We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS"
Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
arXiv Detail & Related papers (2021-06-20T10:48:49Z) - 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.