# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 4 Experimental Results
### Social Media Dataset  
To assess the effectiveness of a new algorithm, it is important to compare its accuracy against established methods using reference datasets. Once it passes this test, the next step is to test it in real-life applications with all the inherent randomness, overlap, and imbalances that only current data can provide. With this in mind, the TSDR was used to classify data from a social network. Advances in technology have significantly improved the processing and storage capabilities of databases in recent years. The limitations of hardware and software have been overcome, making it easier to manage and expand large datasets. The increased processing power and the wider availability of cloud computing services have also made powerful technological resources more accessible. As a result, the combination of large databases and high-performance computers has made it possible to develop previously unimaginable applications [28]. Social networks, born from these technological advancements, are a treasure trove of information that is regularly mined by organizations seeking to better understand their consumers, competitors, and target audience. They provide insights into users' interests and preferences, which can be used to understand and influence behavior. The applications are diverse, ranging from analyzing product or service receptivity, segmenting audiences for advertisements, or disseminating content to groups that are resistant to it [29]. Social networks have transformed society in the 21 st century. The presidential elections in the United States and Brazil in 2016 and 2018, respectively, are strong examples of how digital strategists can guide public opinion, just as a conductor leads an orchestra. These professionals often use the services of data brokers, companies that aggregate and commercialize treated and enriched information from various sources, such as social networks, websites, and apps. An example of the actions of these data brokers is the controversy surrounding the relationship between Cambridge Analytica (London, UK), a British political marketing company, and Facebook, where the data of over 50 million people was illegally collected via personality tests. The quiz, seemingly harmless, was able to quickly profile users based on information such as page likes and posts, obtaining not only the data of those who filled out the forms, but also the entire network of contacts of the participants. In this specific case, the data were allegedly used to outline the profile of the US population during [PERSON]'s presidential campaign in 2016, allowing for more efficient political advertising and targeted ads [30]. This was not the only controversy involving social networks during the same election. Russian interference, also widely reported, was subject to scrutiny by the authorities. A document published by the United States Senate Intelligence Committee concluded that Instagram was just as important as Facebook in influencing the results of the last presidential campaign. The Internet Research Agency, a Russian company involved in digital influence operations on behalf of Russian political and commercial interests, sought to divide the American population using false information and adulterated content. The agency conducted more operations on Instagram than on any other social network, including Facebook, according to reports by the commission. From 2015 to 2018, there were 187 million interactions on Instagram, 77 million on Facebook, and 73 million on Twitter [31]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Original** & **Stage 1** & **Stage 2** \\ \hline cancer\_dataset & 0.71 & 0.73 & 0.83 \\ crab\_dataset & 0.01 & 0.40 & 0.70 \\ glass\_dataset & 0.55 & 0.56 & 0.73 \\ ovarian\_dataset & 0.46 & 0.35 & 0.65 \\ \hline Overall & 0.40 & 0.49 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Silhouette coefficients.