## 3 Related Work
### Privacy Issues Regarding Collected Data  
When looking at user profiling and service personalization, related research shows that users are generally concerned about what happens with their personal data, how the data are stored, who has access to the data and what are the safeguards for protecting their data. This is partially due to the awareness raised by numerous data breaches of different Internet platforms that have happened over the years [25]. Privacy issues regarding the collection of data in the user profiling process can lead to various consequences related to the leakage of personal data, both for individuals and businesses. These consequences to individuals can be related to identity theft, financial loss, discrimination, reputational damage, unauthorized access to personal accounts, loss of privacy, and so on. On the other hand, organizations may face a lot of consequences as well, including loss of customer trust and reputation, infringement of intellectual property, disruption of operations, regulatory penalties, compliance violations, etc. The identification of such consequences provides a more thorough understanding of the privacy concerns and serves to emphasize the importance of data protection for individuals and organizations [26]. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref. No.** & **Types of User-Related Data** &
\begin{tabular}{c} **How Are Data Collected and** \\ **Collection Mechanism?** \\ \end{tabular} & **Internet Platform** \\ \hline
[20] & Demographic data (the user’s personal information, such as age, gender, education, and personality) & The data are gathered from user’s profiles from a Tweet’s text through a Machine Learning models for detecting social bots on Twitter. & Twitter \\ \hline
[21] & User behavior data (e.g., personal profile in Facebook, liked posts, links, status tagging of other users in photos, etc.) & Personality traits prediction model based on user behaviors on social networks & Facebook \\ \hline
[22] & Demographic data (e.g., name, gender, age, native language, education, family members, etc.) & The data are collected through a hybrid profiling system (explicit and implicit user profile). The implicit user profile is created by collecting user’s data based on their interests in terms of keywords, while implicit user profile is created by monitoring user activities by storing the browsing history, the time spent on each page, printing, saving, etc. & Different Internet platforms \\ \hline
[23] & Preference data (e.g., news data and political preferences of users) & The data are collected automatically through algorithms used by Facebook. The number of liked pages related to news and politics assigned is highly correlated with the number of topics related to news and politics assigned to each user. These topics do not represent ads that individuals have seen, but topics that may be interested in. & Facebook \\ \hline
[24] & Demographic and preference user data (e.g., user tweets, user follower’s tweets, URLs posted by user followers, URLs posted by followers, etc.) Also, a Deep Learning approach was used to extract status updates, profile photos, and page likes from a Facebook user’s profile. & Data are collected through a Statistical Based Approach and Deep Learning approach. Also, a variety of information is extracted through a Neighborhood Approach, including user interests, the number of followers, the strength of the relationship between the person and their followers, and much more. & Facebook and Twitter \\ \hline \end{tabular}
\end{table} 
Table 2: Some selected works on collected user data for user profiling in social networks. Some of the most well-known breaches are Yahoo (2013-2014), Equifax (2017), Marriot International hotels (2014-2018), Facebook-Cambridge Analytica (2013-2018), Capital One (2019), SolarWinds (2020), etc. In 2016, Yahoo informed the public that it had multiple data breaches between 2013 and 2014.