# An Anomaly Detection Framework for Twitter Data
## 2 Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Author [Ref.]** & **Activity** & **Technique Used** & **Data Source** & **Description** & **Applications** \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & \multirow{4}{*}{Social media analysis} & \multirow{4}{*}{Graph-based approach.} & \multirow{4}{*}{Social platforms} & \multirow{4}{*}{Multimodal approach used to detect anomalies in posts, users and hashtags.} & \multirow{4}{*}{Rumour Detection} \\  & & & & Data augmentation approach used to model human & \\  & & neural network with a contrastive loss & \multirow{4}{*}{Amazon, Flickr, Facebook, Enron, Twitter dataset} & \multirow{4}{*}{Knowledge and employed use reconstruction loss} & \multirow{4}{*}{Network Anomaly Detection} \\  & & & & & to obtain anomaly scores & \\ [PERSON] et al. [22] & \multirow{4}{*}{Penalized- regression model} & \multirow{4}{*}{Streaming data model} & \multirow{4}{*}{Proposed a real-time adaptive component} & \multirow{4}{*}{Estimation, Forecasting, and Anomaly Detection} \\  & & & & Identified MOOC & \\ [PERSON] et al. [23] & \multirow{4}{*}{LDA for topic modeling} & \multirow{4}{*}{Topic/Document/ Sentence (TDS) Model} & \multirow{4}{*}{IMDB dataset} & \multirow{4}{*}{Topic-based sentiment analysis performed Topic-based LDA (to improve supervised learning) were delayed to extract the features of \(\quad\) & \multirow{4}{*}{Sentiment analysis performed Topic-based LDA (to improve supervised learning) were delayed to extract the features of \(\quad\) & \multirow{4}{*}{Indexing Academic Research Results} \\  & & & & Developed an LDA based decision support system & \\ [PERSON] and [PERSON] [25] & \multirow{4}{*}{LDA} & \multirow{4}{*}{CFPB dataset} & \multirow{4}{*}{Deep model to improve supervised learning) were delayed to extract the features of \(\quad\) & \multirow{4}{*}{Analysis of consumer complaints} \\  & & & & Deployed LDA for topics modeling and performed on Weibo \\ [PERSON] and Al-Khalifa [28] & \multirow{4}{*}{NMF for topic modeling} & \multirow{4}{*}{BERTopic Arabic language} & \multirow{4}{*}{Arabic language} & \multirow{4}{*}{Topic modeling for Arabic Language models} & \multirow{4}{*}{Topic modeling for Arabic language} \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of recent development on LDA and NMF for topic modeling. with text. Based on words, this method was used to identify the topics a document contains. LDA is composed of two steps: first, words already known, and second, words related to a topic where the probability of belonging must be computed. Along with LDA, NMF is a popular statistical topic modeling approach based on factor analysis. NMF assigns weight to each word based on coherence. TF-IDF computes the importance of a word in a given document and converts it into a vector with a numerical value. TF-IDF extracts the weighted features from a Tweet dataset and provides the weighting of each word in the corpus, which improves the learning feature of the model's performance. BERT architecture is a collection of various transformer encoders stacks, and has a feed-forward layer and a self-attention layer. This method is used to find the relationship between the words of a sentence and divides them into two categories: normal and anomaly.