A r COV -19: The First A rabic COVID -19 T witter Dataset with Propagation Networks (2021)

Abstract: In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and -liked). The propagation networks include both retweetsand conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and the propagation networks, we also release the search queries and the language-independent crawler used to collect the tweets to encourage the curation of similar datasets.

## introduction twitter streams hundreds of millions of tweets daily. in addition to being a medium for the spread and consumption of news, it has been shown to capture the dynamics of real-world events including the spread of diseases such as the seasonal influenza @xcite or more severe epidemics like zika @xcite , ebola @xcite , and h1n1 @xcite . moreover, collective conversations on twitter about an event can have a great influence on the event's outcomes, e.g., us 2016 presidential elections @xcite . analyzing tweets about an event, as it evolves, offers a great opportunity to understanding its structure and characteristics, informing decisions based on its development, and anticipating its outcomes as represented in twitter and, more importantly, in the real world. since the first reported case of novel coronavirus in china, in november 2019, the covid-19 topic has drawn the interest of many arab users over twitter. their interest, reflected in the arabic content on the platform, has reached a peak after two months when the first case was reported in the united arab emirates late in january 2020. this ongoing pandemic has, unsurprisingly, spiked discussions on twitter covering a wide range of topics such as general information about the disease, preventive measures, procedures and newly-enforced decisions by governments, upto-date statistics of the spread in the world, and even the change in our daily habits and work styles. in this work, we aim to support future research on social media during this historical period of our time by curating an arabic dataset (arcov-19) that exclusively covers tweets about covid-19. we limit the dataset to arabic since it is among the most dominant languages in twitter @xcite ), yet under-studied in general. arcov-19 is the first arabic twitter dataset designed to capture tweets discussing covid-19 starting from january 27 th 2020 till end of january 2021 1 , constituting about 2.7m tweets, alongside the propagation networks of the most-popular subset of them. to our knowledge, there is no publiclyavailable arabic twitter dataset for covid-19 that includes the propagation networks of a good subset of its tweets. some existing efforts have already started to curate covid-19 datasets including arabic tweets, but arabic is severely under-represented (e.g., @xcite ) or represented by a random sample that is not specifically focused on covid-19 (e.g., @xcite ), or the dataset is limited in the period it covers and does not include the propagation networks (e.g., @xcite ). 1 the dataset will be continuously augmented with new tweets over the coming months. the contribution of this paper is three-fold: the reminder of the paper is organized as follows. we present the crawling process followed to acquire arcov-19 in section 3. we then thoroughly discuss the analysis that we conducted on the dataset in section 4. we suggest some use cases to enable research on arabic tweets in section 5. we finally conclude in section 6. ## related work social media platforms and twitter specifically showed to be an indispensable medium for sharing information and discussions about the covid-19 pandemic since december 2019. a considerable body of raw social media datasets were released to facilitate analyzing these discussions including twitter datasets @xcite @xcite @xcite @xcite , instagram @xcite , or the chinese social media platform weibo @xcite . banda et al. (2020) released a multilingual dataset that includes over 800m tweets, the data is collected since 1 st of january 2020 using twitter streaming api. @xcite , collected multilingual tweets posted between march and july 2020. the most dominant languages covered by their dataset are english and spanish constituting 65% and 12% of the tweets respectively. @xcite are continously collecting tweets since 21 st of january 2020, where at the time of writing, around 66% and 11% of the tweets are in english and spanish respectively. another multilingual ongoing collection is released by @xcite . @xcite al. (2020) ## data collection arcov-19 includes two major components: the source tweets (i.e., tweets collected via twitter search api every day) and the propagation networks (i.e., retweets and conversational threads of a subset of the source tweets). in this section, we present how we collected each in sections 3.1, and 3.2 respectively, and summarize the released data in section 3.3. ## arcov-19 in numbers in this section, we present a statistical summary and conduct an analysis on arcov-19 to shed some light on its major characteristics. ## enabling research twitter in particular) to the time of this writing. to enable research on different tasks on arabic tweets, we make arcov-19 publicly-available for the research community. having quantified the topical, geographical, and community representativeness of arcov-19, we envision that it is suitable for diverse natural language processing, information retrieval, and computational social science research tasks including, but not limited to, emergency management, misinformation detection, and social analytics, as we discuss below. furthermore, we provide propagation networks of daily highly popular subset (the most popular 1k) to ensure the quality of tweets. this sample is drawn after filtering out potential low-quality tweets (e.g., spam) and content duplicates. we anticipate the top subset and the network to support these tasks on the popular arabic tweets that we assume have the most effect on shaping public opinion and understanding. ## conclusion in this paper, we presented arcov-19, the first arabic twitter dataset about the novel coronavirus (covid-19) that includes propagation networks of a large subset of tweets. we release all source tweets, top subset, search queries, and the propagation networks. preliminary analysis showed that arcov-19 captured spikes in tweeting frequency for country-specific tweets that are consistent with the first reported cases of covid-19 in several arab countries. we also found dominance of news agencies among top tweeting users and among most shared urls. arcov-19 enables research under many domains including natural language processing, information retrieval, and social computing. we plan to continue collecting tweets for the foreseeable future and the dataset will be continuously updated with newly collected tweets and propagation networks.