For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis
of a Watershed Moment in Iran's Gender Struggles
- URL: http://arxiv.org/abs/2307.03764v1
- Date: Fri, 7 Jul 2023 19:39:15 GMT
- Title: For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis
of a Watershed Moment in Iran's Gender Struggles
- Authors: Adel Khorramrouz and Sujan Dutta and Ashiqur R. KhudaBukhsh
- Abstract summary: Mahsa Amini's death triggered polarized Persian language discourse.
Both fractions of negative and positive tweets toward gender equality increased.
Pro-protest accounts are more similar to baseline Persian Twitter activity.
- Score: 10.474108328884807
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we present a computational analysis of the Persian language
Twitter discourse with the aim to estimate the shift in stance toward gender
equality following the death of Mahsa Amini in police custody. We present an
ensemble active learning pipeline to train a stance classifier. Our novelty
lies in the involvement of Iranian women in an active role as annotators in
building this AI system. Our annotators not only provide labels, but they also
suggest valuable keywords for more meaningful corpus creation as well as
provide short example documents for a guided sampling step. Our analyses
indicate that Mahsa Amini's death triggered polarized Persian language
discourse where both fractions of negative and positive tweets toward gender
equality increased. The increase in positive tweets was slightly greater than
the increase in negative tweets. We also observe that with respect to account
creation time, between the state-aligned Twitter accounts and pro-protest
Twitter accounts, pro-protest accounts are more similar to baseline Persian
Twitter activity.
Related papers
- Russo-Ukrainian War: Prediction and explanation of Twitter suspension [47.61306219245444]
This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of user accounts that may lead to this.
We have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API.
Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin fraud, spam, and advertisement campaigns.
arXiv Detail & Related papers (2023-06-06T08:41:02Z) - Identification of Twitter Bots based on an Explainable ML Framework: the
US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data.
Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm.
Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z) - Discourse Analysis of Covid-19 in Persian Twitter Social Networks Using
Graph Mining and Natural Language Processing [0.0]
The examined big data is five million tweets from 160,000 users of the Persian Twitter network.
The analyzed Iranian society does not consider itself responsible for the Covid-19 wicked problem.
The most active and most influential users' similarity is that political, national, and critical discourse construction is the predominant one.
arXiv Detail & Related papers (2021-09-01T10:39:20Z) - Towards A Sentiment Analyzer for Low-Resource Languages [0.0]
This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time.
We use the hashtag textit#kpujangancurang that was the trending topic during the Indonesia presidential election in 2019.
This research utilizes rapid miner tool to generate the twitter data and comparing Naive Bayes, K-Nearest Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to classify the sentiment of the twitter data.
arXiv Detail & Related papers (2020-11-12T13:50:00Z) - Understanding the Hoarding Behaviors during the COVID-19 Pandemic using
Large Scale Social Media Data [77.34726150561087]
We analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020.
We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population.
The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean.
arXiv Detail & Related papers (2020-10-15T16:02:25Z) - LEBANONUPRISING: a thorough study of Lebanese tweets [0.0]
On October 17, Lebanon witnessed the start of a revolution; the LebanonUprising hashtag became viral on Twitter.
A dataset consisting of a 100,0000 tweets was collected between 18 and 21 October.
We conducted a sentiment analysis study for the tweets in spoken Lebanese Arabic related to the LebanonUprising hashtag using different machine learning algorithms.
arXiv Detail & Related papers (2020-09-30T05:50:08Z) - Is Japanese gendered language used on Twitter ? A large scale study [0.0]
This study starts from a collection of 408 million Japanese tweets from 2015 till 2019 and an additional sample of 2355 manually classified Twitter accounts timelines into gender and categories (politicians, musicians, etc)
A large scale textual analysis is performed on this corpus to identify and examine sentence-final particles (SFPs) and first-person pronouns appearing in the texts.
It turns out that gendered language is in fact used also on Twitter, in about 6% of the tweets, and that the prescriptive classification into "male" and "female" language does not always meet the expectations, with remarkable exceptions.
arXiv Detail & Related papers (2020-06-29T11:07:10Z) - Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in
Iran using NLP [0.6606745253604263]
Using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users.
We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point.
While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.
arXiv Detail & Related papers (2020-05-17T23:47:08Z) - Detecting Perceived Emotions in Hurricane Disasters [62.760131661847986]
We introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria.
We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups.
arXiv Detail & Related papers (2020-04-29T16:17:49Z) - 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) - #MeToo on Campus: Studying College Sexual Assault at Scale Using Data
Reported on Social Media [71.74529365205053]
We analyze the influence of the # trend on a pool of college followers.
The results show that the majority of topics embedded in those # tweets detail sexual harassment stories.
There exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions.
arXiv Detail & Related papers (2020-01-16T18:05:46Z)
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.