### Relevance of Mining and Analysis of Social Media Data during Virus Outbreaks  
Throughout the annals of history, humanity has grappled with numerous outbreaks of infectious diseases, each inflicting a toll on lives and global economies. Within this context, social media platforms have emerged as invaluable sources of information, capable of offering deep insights into the characteristics and status of such outbreaks. Leveraging the power of text mining, health-related data can be gleaned from platforms like Twitter [45; 46; 47]. The comprehensive information contained within Twitter data grants researchers access to different forms of web behavior on the internet [48; 49] and user-generated content [50; 51], facilitating early response strategies and informed decision-making. The realm of social media mining assumes a pivotal role in monitoring diseases and gauging public awareness of health concerns, thereby enabling proactive disease forecasting [52]. It is worth emphasizing that text analyses of Twitter data have become a focal point in the realm of medical informatics research [53].  
The utilization of social media data to conduct syndromic surveillance, with a keen focus on public health-related matters through web-based content, holds paramount importance [54; 55]. A significant rationale underpinning this approach is the realization that during an outbreak, social media platforms serve as vital conduits for real-time public sentiment, offering a window into the prevailing concerns and anxieties through user comments and outreach. Of these social media platforms, Twitter stands out as a prominent communication channel during disease outbreaks [56]. Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57; 58; 59; 60; 61], cryptocurrency and stock markets [62; 63], public health concerns [64; 65; 66; 67], societal problems [68; 69; 70; 71; 72], emerging technologies [73; 74], human behavior analysis [75; 76; 77; 78], and humanitarian issues [79; 80; 81; 82; 83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84; 85]. However, it is important to note that there is still very little research on Long COVID. Additionally, there has not been any work in this area that has emphasized the study of Tweets wherein individuals self-reported Long COVID. This study aims to address these research gaps by presenting multiple novel findings from a comprehensive analysis of a dataset consisting of 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023.  
The remainder of the paper is presented as follows. Section 2 presents a comprehensive review of recent works in this field. A detailed description of the methodology used for this research work is presented in Section 3. In Section 4, the results of this study are described, and the salient contributions of this paper are highlighted. This section also includes a discussion on how the findings of this study help to interpret global public opinion towards Long COVID, and how the contributions of this study are expected to be helpful for public health agencies. Section 4 is followed by the conclusion section, wherein the plans for future work are also stated.