Coronavirus: Public Arabic Twitter Dataset

Addawood, Aseel

doi:10.20944/preprints202004.0263.v1

Cited by 8 publications

(10 citation statements)

References 1 publication

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“…ArCov-19 [21] January-June 2020 785,000 ArCorona [22] 21 February-31 March 2020 1,000,000 Addawood's Dataset [23] January-April 2020 3,800,000 Alam [24] January-April 2020 218 Alsudais Dataset [9] December 2019-April 2020 1,048,575 While an infodemic cannot be eliminated, it can be managed with the right precautions. The World Health Organization has led the efforts in this field to guide relevant research and effective practices across the globe and define public health research needs in order to advance this field during the COVID-19 pandemic.…”

Section: Dataset Timeframe Tweetsmentioning

confidence: 99%

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A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

et al. 2021

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COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.

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Section: Dataset Timeframe Tweetsmentioning

confidence: 99%

“…Data was collected on Twitter from the beginning in December 2019 to 10 April 2020, using several keywords related to the pandemic in Arabic [23]. The dataset was collected by identifying a list of popular hashtags and keywords used mainly by the public in the local context of Saudi Arabia.…”

Section: Twitter Datamentioning

confidence: 99%

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

et al. 2021

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“…The hashtags were categorized based on their meaning, how users were interacting with it and who started the hashtags. The categorization was done by Addawood (2020). Table 1 shows a sample of the hashtags.…”

Section: Datamentioning

confidence: 99%

Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case

Addawood¹,

Alsuwailem²,

Alohali³

et al. 2020

Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

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The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.

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“…Locations of widespread infections, people's reactions to quarantine procedures, symptoms of the new variants of the virus, and the psychological effects can be highlighted from the text analysis of social media data to support real-time disaster operation and management [3][4][5][6]. In [7], the author states that the ministry of health of Saudi Arabia has used several accounts on Twitter to populate many health-related hashtags to provide governmental pieces of advice to Saudi Arabian citizens. This is evidence that decision-makers are considering social media as important channels for communicating with people.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we introduce our efforts for analyzing COVID-19 Arabic conversations on the Twitter network. We used a large dataset of Arabic tweets related to the topic of COVID-19 [7]. It has been crawled in the period from January to April 2020.…”

Section: Introductionmentioning

confidence: 99%

Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

et al. 2021

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The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.

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Coronavirus: Public Arabic Twitter Dataset

Cited by 8 publications

References 1 publication

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case

Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

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