Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP

• URL: http://arxiv.org/abs/2005.08400v1
• Date: Sun, 17 May 2020 23:47:08 GMT
• Title: Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP
• Authors: Pedram Hosseini and Poorya Hosseini and David A. Broniatowski
• Abstract summary: 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.
• Score: 0.6606745253604263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized broader content of tweets that shows satire, followed by news, is the dominant tweet type among the Iranian users. 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.

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)
• Extracting Feelings of People Regarding COVID-19 by Social Network Mining [0.0]
  dataset of COVID-related tweets in English language is collected. More than two million tweets from March 23 to June 23 of 2020 are analyzed.
  arXiv  Detail & Related papers  (2021-10-12T16:45:33Z)
• Extracting Major Topics of COVID-19 Related Tweets [2.867517731896504]
  We use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We additionally analyze temporal trends of the topics for the whole world and four countries.
  arXiv  Detail & Related papers  (2021-10-05T08:40:51Z)
• The Spread of Propaganda by Coordinated Communities on Social Media [43.2770127582382]
  We analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities.
  arXiv  Detail & Related papers  (2021-09-27T13:39:10Z)
• 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)
• 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)
• ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic [3.057212947792573]
  We present the largest manually annotated dataset of Arabic tweets related to COVID-19. We describe annotation guidelines, analyze our dataset and build effective machine learning and transformer based models for classification.
  arXiv  Detail & Related papers  (2020-12-02T19:05:25Z)
• 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)
• Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions [44.92240076313168]
  We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
  arXiv  Detail & Related papers  (2020-05-29T09:24:38Z)
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis [51.39895377836919]
  COVID-19 has sparked racism and hate on social media targeted towards Asian communities. We study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
  arXiv  Detail & Related papers  (2020-05-25T21:58:09Z)
• COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations [22.43295864610142]
  We collected streaming data related to COVID-19 using the Twitter API, starting March 1, 2020. We identified unreliable and misleading contents based on fact-checking sources. We examined the narratives promoted in misinformation tweets, along with the distribution of engagements with these tweets.
  arXiv  Detail & Related papers  (2020-03-26T09:48:24Z)

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.