## 3 Dataset Analysis
### Analysis of the Literature
#### 3.4.2. Top 10 Most Cited Papers--A Review
Additionally, the process of gathering data is quite thorough, encompassing steps like tokenization, stemming, and aggregation. Thus, the three datasets are divided as follows: 148,652 tweets related to the FA Cup, 474,109 tweets related to Super Tuesday, and 1,247,483 tweets related to the US Elections. The results of the experiment suggest that BNgram performs better, especially in events with a more concentrated theme field, whereas standard topic models (e.g., LDA) function well in events with a tight subjective emphasis. The development and implementation of a real-time crisis-mapping system is the main focus of the [PERSON] et al. [39] research. Two sets of data associated with particular disasters in Milan, New York, and Istanbul are collected from the messages posted on social media platform Twitter, and then used for building as well as evaluating the system's efficiency. After performing pre-processing and Treebank word tokenization via the Punkt sentence tokenizer, the location matching algorithm is used. The system's correctness is evaluated and validated using metrics including precision, recall, and F1. The outcome of the investigation demonstrates that the present approach achieves far better values for tweet geoparsing than other solutions currently in use, highlighting the research's significance for the scientific community. Undoubtedly, the study performed by [PERSON] et al. [40] is another highly intriguing article, relevant to our bibliometric analysis around the field of natural language processing in social media research. Its goals include helping the authorities identify the factors that influence users to repost specific information and assisting the government in the development and improvement of strategies related to the publication of information about the COVID-19 pandemic. The dataset consists of postings connected to COVID-19 that were gathered from the Weibo platform using the Weibo API. The posts were then categorized through the use of natural language processing techniques, supervised learning algorithms, and linguistic inquiry and word counting. The study's findings showed that the use of hashtags increased notification reposts; a positive tone amplified the popularity of criticizing opinions; reposts of longer messages happened a greater number of times; and information exposing rumors was more likely to be reposted when it came from people with a wide number of followers. All of the aforementioned findings may provide valuable insights for the authorities and can serve as a foundation in the development of more effective strategies related to the dissemination of information associated with the COVID-19 pandemic. An intriguing topic--namely, how the presence of an image in a post influences user involvement--is highlighted in the following [PERSON] et al. [41] paper, which grabs the reader's interest from the beginning of its contents by being extremely well organized and meticulously detailed. The study focuses on two popular social media platforms, Twitter and Instagram, and the collected datasets are related to US airlines and compact SUV models. Furthermore, three primary effects of picture content are hypothesized: mere presence effect, image characteristics effect, and image-text fit effect. The methods used include propensity score matching and the bivariate zero-inflated negative binomial for Twitter data, while for Instagram, log-linear regression is used. The results of the research reveal that when images are included in postings, users seem to be more involved, demonstrating that factors consisting of the source, quality, and color effects have a direct impact on users' level of engagement. Additionally, the study provides evidence that hyperlinked pictures have negative consequences, suggesting that a substantial number of users are unwilling to allocate extra effort for viewing an image. Lastly, the researchers provide helpful user strategies and underscore the significance of visual content. In the following research, [PERSON] et al.