# Credibility Analysis of User-Designed Content Using Machine Learning Techniques
## 2 Literature Survey  
This section covers the research in the field of information credibility, fake news detection, and trust in social media content. The majority of the research articles used are from 2019 to 2021. Author [PERSON] et al. [1] focused on the WELFake approach based on the classification of fake news using the voting method. The 20 shortlisted features are used then a weighted classification of selected features delivers high accuracy. [PERSON] et al. [2] say clickbait is the design of the content to attract the readers. The study suggests a relation between the clickbait mechanism and readers' correlation. [PERSON] [13] assigns the score to the article to understand the trustworthiness of the article. The author believes that the credibility of an article cannot be simply calculated using a machine. Human interference is necessary to decide the credibility of the content. [PERSON] et al. [3] designed the FakeBERT model to classify fake news using CNN, where the features are detected automatically. [PERSON] et al. [13] used 10,000 samples from Twitter and Facebook, 56 accounts with 23,489 messages for performing the classification using Naive Bayes (NB), Support Vector Machine, (SVM), Logistic Regression (Logit), and the J48 algorithm (J48). Seventeen new features for Twitter and 49 features for Facebook used along with spam and sentiment are also considered. The output of the experiment is a message and the user credibility. Results are compared with earlier classifier accuracy. [PERSON] et al. [9], the sampling is selected to represent the entire USA population, the professional research firm GfK's probability-based participants are used. The questionnaire is prepared with a logic to address a multi-step and multi-method process including feedback, and direct interviews. The author compared context with all sides of the content using [PERSON]'s scale. The features are Message sidedness, Flexible thinking (FT), Need for cognition, and Interaction.  
Depending upon the interaction and whether it is a two-way or three-way interaction, the balance or inclination towards the specific opinion is decided; for validation, ANOVA is used. [PERSON] [10] performed a survey of 1207 participants first and then 603 participants. Analysis suggests that the Twitter accounts are available in 60% of the 603 participants, who are more educated. According to the author, credibility depends upon the tweet, media literacy, platform, and misinterpretations. The accuracy of the model is decided using the Chi-Square testing method. Additionally, recall helps in deciding the coverage of the total information. [PERSON] et al. [11] address one of the important issues--Effective crises management through the narrators. To accomplish this the task force of adults in the U. S. is asked to perform a survey. This survey is performed considering seven different points. Crisis information-seeking intentions, emotions, government responsibility attribution, and perceived information credibility, are the parameters for studying the credibility of the narrator [14]. The literature survey led to an important observation about the sources and the type of dataset useful for the experiment. Different types of Linguistic, platform-specific, and user-specific features are analyzed. The approach to deciding the credibility based on the survey, machine learning, or a combination of both was discussed. Details of the scale for the survey and techniques to perform the survey are observed. Machine learning techniques and the use of specific algorithms depended upon classification and regression. The potential threat of not addressing the content modeling is noted.