## 2 Literature Review  
An overview of recent studies in this area, with a particular emphasis on the study of social media discussions regarding COVID-19 and Long COVID, with Twitter serving as the key platform of interest, is outlined in this section. [PERSON] et al. [86] used the k-kNN approach to classify COVID-19-related Tweets into three separate classes--positive, negative, and neutral. Sorting through pertinent Tweets was the first stage in their investigation. After the Tweets had first been filtered, the kNN approach was used to conduct a detailed investigation of the opinions expressed in these Tweets. [PERSON] et al. [87] analyzed Tweets in which people expressed a desire to go to Bangkok during the COVID-19 outbreak. The authors applied an SVM-based classifier and were able to categorize the Tweets as positive, negative, and neutral. The trending themes about COVID-19, as expressed on Twitter, were identified and investigated by [PERSON] et al. [88]. Their work involved deducing the overarching concerns expressed in those Tweets. [PERSON] et al. 's approach [89] involved using a directed acyclic network model to identify abnormalities associated with events related to COVID-19 as communicated on Twitter. The focus of [PERSON] et al. 's study [90] was the development of a method specifically designed to examine Tweets related to COVID-19 that had a strong connection to the recommendations made by the Centers for Disease Control and Prevention (CDC) regarding COVID-19. Their main objective was to identify the range of public opinions, including worries, interests, and demands, related to the guidelines of the CDC for COVID-19. [PERSON] et al. 's research [91] included filtering and examining Tweets about COVID-19 from 1 January 2020 to 27 October 2020. In their research, the authors specifically analyzedTweets in which people expressed varied opinions on whether wearing masks effectively slowed the spread of COVID-19. [PERSON] et al. [92] devised a technique to assign an influence score to individuals who published COVID-19-related Tweets on Twitter. Their research also involved identifying prominent users on Twitter who actively participated in the COVID-19 dialogue on Twitter and contributed towards impacting the global discourse and sentiments. [PERSON] et al. [93] used multiple concepts of machine learning and data analytics and developed a misinformation detection classifier that examined Tweets about COVID-19 using the random forest approach. The accuracy of this classifier was observed to be 79%. [PERSON] et al. [94] developed a comprehensive system designed to delve into the expansive realm of Twitter discourse, wherein individuals voiced their opinions concerning the COVID-19-induced lockdown measures in the United States. Their aim was to gain insights into the multifaceted array of sentiments, perspectives, and reactions related to the pandemic response. [PERSON] et al. [95] developed a framework to analyze the collective mindset of the Indian population, as reflected in their Tweets during the two nation-wide lockdowns enforced by the Indian government in response to the COVID-19 crisis. [PERSON] et al. [96] developed a systematic approach geared towards the identification and examination of COVID-19-related Tweets, wherein the Twitter community engaged in dialogues surrounding issues of addiction. This initiative provided a unique window into the evolving discourse on addiction amidst the backdrop of a global pandemic. [PERSON] et al. 's explorative study [97] centered on studying Tweets wherein individuals shared heart-wrenching accounts of their loved ones succumbing to COVID-19. Their particular focus honed in on those instances wherein patients faced their final moments in isolation. This research illuminated the profoundly emotional and isolating aspects of the pandemic's impact. [PERSON] et al. [98] endeavored to study Tweets containing specific keywords, spotlighting conversations among Twitter users grappling with profound feelings of loneliness during the COVID-19 era.