## 3 Dataset Analysis
### Analysis of the Literature
#### 3.4.2. Top 10 Most Cited Papers--A Review
Taking advantage of an extensive dataset of 106,316 messages gathered from 782 distinct companies on a widely accessed online social media platform, Facebook, the analysis aims to comprehend the ways in which various content attributes impact user interactions, illustrated by metrics consisting of likes, comments, shares, and clicks. The study combines natural language processing (NLP) and content analysis to evaluate the textual content of some specific posts, producing 16 binary attributes per message. If one is interested in the results obtained, the paper indicates that people seem to appreciate and interact more with posts that are funny or emotionally oriented and that connect to the personality of the company, rather than merely informative materials, even if, however, certain types of informational material may increase click-through rates. Furthermore, another point addressed in the manuscript is related to the significance of Facebook's EdgeRank algorithm in impacting user engagement and content exposure, accentuating the need for marketers to fully understand and act within the platform's dynamics in order to produce successful content strategies. Long-term brand growth depends on finding a balance between content that addresses the personality of the brand and information material that supports performance marketing goals. The next article belonging to [PERSON] et al. [36] is a fascinating manuscript, which, in comparison to the previous ones, is focused on the health domain, more specifically, on the pharmaceutical industry. The researchers propose an innovative artificial-learning-based system referred to as ADRmine, whose primary goal is to extract messages from social networks that mention the adverse reactions experienced by consumers associated with specific drug experiences. The data used for training consist of two corpora: Twitter and DailyStrength (DS), each of them with a set of training and a set of testing, containing messages extracted from online posts. Furthermore, the research employs a variety of techniques, including context, ADR lexicon, parts of speech, and negation identification, while, in order to assist the system for handling unseen tokens, word embeddings and clustering techniques are involved. In terms of the outcomes, the model performs better than anticipated, proving its enhanced effectiveness in collecting messages with respect to the negative effects of drugs from social media platforms, recording considerably better values than basic methods for crucial metrics like precision, recall, and the F-measure. Rather than focusing on building a new model to identify particular information, the [PERSON] et al. [37] article, in contrast to the other papers presented in this section, provides a thorough overview of the existing literature in the scientific community regarding the detection and resolution of rumors in social networks. The researchers explore rumor classification systems, including their detection and categorization, through in-depth evaluations of numerous approaches across multiple datasets. Furthermore, potential areas for further research are highlighted in addition to the existing challenges. The purpose of the article is not to report experimental results but to give a comprehensive guide for the scientific community by combining and contrasting significant facts from other prior studies. The next article is written by [PERSON] et al. [38] and brings to light a different approach of natural language processing in social media research, in an original manner. Starting from six detection algorithms, latent Dirichlet allocation (LDA), document-pivot (Docp), graph-based feature-pivot (GFeat-p), frequent pattern mining (FPM), sequential FPM (SFPM), and BNgram, the researchers aim to offer an expanded understanding of each algorithm's performance in the subject of topic detection in messages collected from the Twitter platform, based on precision and recall metrics.