Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach

Introduction

Today Internet communication is the smart connectivity link between different people. In olden days, communication was carried out with letters, post cards, telegrams, and land telephones. People from different place communicated through any one of many types of communication. In cases of message delivery or emergency information sharing, data can be shared either with the postal card delivery or telegram message delivery. In these cases, accessing the communication particulars sometimes got delayed because of unconditional weather conditions or unpredictable situations. In this scenario, people may be adversely affected by the delay of receiving valuable information.

To accomplish quick communication, the technology can be improved with the digital and information technology techniques. Using Internet and intranet technologies, information can be accessed from anywhere at any point of time. Initially, the widespread launch of the Internet has spread communications from people from remote resources. Now the Internet has spread and diversified and can be accessed with social media networks either using web technologies or mobile applications. People are accessing quickly relevant messages with social networks like Facebook, Twitter, WhatsApp, Instagram, Skype, Google+, and so on.

These social networks may play a vital role in every human being’s life to tackle their day-to-day life information updates. In particular, the social networks can have both advantages and disadvantages. The advantages include the diversity of people’s information sharing: quick posting of messages, likes, and updates of posts as well as delivering products for business landlords. People have the fast communication and accessibility of information from any sort of networks. The scenario also has disadvantages, like misuse of data, privacy, posting of the other’s user account data, and information hacking. These can negatively affect the social networking user’s life and create many unsolvable problems. The major area affected by this is the misuse of the person’s login identification, such as one person’s login credentials being used by the another person and misusing a person’s data information.

There are many possibilities to hack data in a social media network. The user should be aware about privacy concerns when posting any personal information in the social network. Some concerns are the misuse of posted photos and hacking personal information in the social networks. Hackers can use hacking techniques and tools to hack any user data. Such hacked information or data is misused by the hackers. Due to these disadvantages, it can be difficult to solve the major issues of the resulting social problems.

Literature Survey

Zhuozhao Li et al (2018) [1] has proposed the concept called the Disney Information Station (DIS boards), a discussion forum for the Disney-related travel for people who visit Disney properties frequently. The DIS system was developed to reduce the search time of Disney-relevant information such as resorts, dining, and other people’s comments to the other visitors. With respect to the usage of DIS, teenagers and adult users have been categorized by the system as clusters of report, fact, and discussion. In the system thread categories are created and communicated for the discussion forum. The cluster analysis called ego network has been created for trip reports, restaurants, and adventures. By the DIS system, the Disney social network users can quickly find relevant information quickly.

Ming Cheung et al (2018) [2] have researched the search engine social network for the west and east content-sharing mechanism. In this mechanism, the authors have referenced eight million people’s usage with eight different social networks. In this work the major analysis started with user-shared images and the friend’s followers. The search engine process began by collecting images from multiple social networks and the images are labeled and discovered with a concept called convolutional neural network. By connecting more than two or three social networks the connectivity and discoverability can be improved for the followers in the social networks.

Rahil Sharma et al (2017) [3] have proposed an algorithm called novel hybrid parallel algorithm to categorize the community of the groups in the social networks. As social networks have developed, diverse parallel algorithms a parallel algorithm with synthetic graphs has been used to categorize the analysis. In this approach, in order to cluster the community groups, the multilevel multicore (MCML) community detection algorithm (shared memory parallel implementation) has been used to divide the groups into clusters. In this the levels are categorized as network partitioning, renumber vertices and image partitions, and MCML. As with the different partitioning, the good scalability and proper quality of community searching can occur in the social communities.

Xue Yung et al (2016) [4] has introduced the protocol called secure and finegrained privacy-preserving matching (SFPM) protocol for secure mobile communications. The protocol was developed to enhance the security of mobile communications. In SFPM, two methods are used to describe the matches between the two mobiles, such as the cosine similarity check and weighted LI norm matching. For the first one, a similar set of objects are identified and measured with the cosine similarity verification. In the second, weighted LI matching was preferred to match the weightages of the object during the communication transmission. As with the usage of the SFPM protocol, only authenticated information was shared safely in Android mobile applications. The SFPM protocol was tested with two types of Android mobile applications and one PC. In this approach the user can secure information in the Androids using SFPM protocols.

Hongjian Wang et al (2019) [5] proposed that crime rate analysis can be executed in different research. In this chapter, the authors proposed the term Point of Interest (POI) data in the area of Chicago. A binomial regression model and a geographically weighted regression model were developed to monitor the features of theft, criminal damage, burglary, and motor vehicle theft. Using the POI approach the crime analysis can be quickly analyzed and rectified.

Daniel Zhang et al (2019) [6] have proposed the scalable and robust truth discovery (SRTD) approach to study identical misinformation spread on the social media. The chapter focuses on Twitter analysis based on the truth fullness scores. The

SRTD can be analyzed with data sparsity analysis and data fusion techniques. Three main observations are monitored and deployed to improve the SRTD algorithm: very lagging independent verification during forwarding the messages, forwarding the false claims, and false consideration about the previous claims. Using this approach the misinformation spread in the social networks Twitter, Facebook, and Instagram were analyzed.

Alamelu Muthukrishnan et al (2017) [7] proposed a method for the most people usage (MPU) Internet of Things structure for the health care and social networking systems. The collective healthcare-related queries are collected from the end user and analyzed with the expert members. The most unsolved queries are input to the proposed Health Social Cloud Center (HSCC) system. The internal system will make the analysis of the risk analysis and expert member discussion with the expert analysis databases. The finalized solution is then sent to the requested client and the customer can make the final decisions.

Usage Analysis and Negotiable (UAN) Approach

In general, many societal issues may occur through misuses of social media networks. To reduce misuse of social networks and to create awareness in teenagers and adults, the proposed usage analysis and negotiable (UAN) approach will propose user view analysis with the user interested test rate (UITR) analysis algorithm approaches. The proposed UAN system may subdivided into a number of submodules categorized by age categorization usage analysis, user view analysis, and UITR analysis algorithm and feedback generation.

In this analysis the end user can use various social networking applications and share their full thoughts in the social networking system. Initially the system will categorize end-user usage with the age categorizations and, based on the category of ages 18-35, it next puts forward to the user view analysis. In this case, the user information sharing can be categorized with the likes and dislikes, content sharing, and comment-posting categories. With respect to the user usage analysis, the UITR algorithm will rate the user usage of the networks to the end users. With this the end users, especially the age group from 18 to 35, can come to the valuable decisions of using the social networks in a good way. The detailed processing UAN approach is depicted in Figure 2.1.

The initial flow of processing of UAN can start from the diverse set of end customers. The customers can choose the social networks. They will use the diverse version of the social network applications such as Facebook, WhatsApp, Instagram. Twitter, Google +, Vimeo, and You Tube.

As the initial step, the age categorization can be carried out with the set of age groupers. The first step can be started from the age categorization. The age categorization can be started with the adult age categorization analysis, in this case the age categorization can be carried out as the group of 18 to 35 age groupers. The age categorization analysis can then be carried out with the categories of 18 to 25, 26 to 30, and 31 to 35.

Usage analysis and negotiable approach

FIGURE 2.1 Usage analysis and negotiable approach.

Age Categorization Usage Analysis

The proposed UAN system will start with the initial age categorization with the category of the age groupers from 18 to 35. This can be divided with the categorization from 18 to 25, 26 to 30, and 31 to 35 groupers. As per the adult age categorization, the rule states for beginners, medium agers, and large age groups.

Rules Defined for 18-25 Age Categorization

This age group is defined as the beginners of the UAN system. In this case, the system will recognize the beginners with the initiation rules for the identification. That is:

  • 1. Beginner has to verify their details with the DOB profile verification.
  • 2. Beginner Entry profile verification = if (DOB = 18 or DOB <= 25) then verify Government ID proof.
  • 3. Else
  • 4. Beginner Entry profile verification = Restricted category
  • 5. The DOB profile verification can linked with any one of the government- defined proof of voter ID or Aadhaar number.
  • 6. If the age categorization does not match with the beginner identification it can be strictly monitored with the policy awareness identification rules.
  • 7. If matching has not been found then the unmatched beginners are put on the restricted categorization.

According to the beginners awareness policies, unmatched beginners are instructed to avoid preregistration and they are put on a waiting list until they meet the eligibility criteria.

Rules Defined for 26–30 Age Categorization

The second category is defined for the middle age group from 26 to 30 years of age. Those grouped in this category have more awareness than the young age group. The 26-30 age group starts with the following rules:

  • 1. Initially verified with the DOB profile verification.
  • 2. With respect to the DOB. any one of the government-defined documents can be verified (as similar to beginners policy)
  • 3. The middle age group is verified with the usage of the social media networks, for example, a count of social networks for a person.
  • 4. Middle age category verification = (Social network account count + Active usage of the social network)/!Person choice of using the social account).
  • 5. If a person has more uncounted social networking accounts (more than 7), the advantages and disadvantages of using the specific social networks can be sent as a report to the end users.

Rules Defined for 31–35 Age Categorization

This rule can be implemented for the 31 to 35 age group. At this age end users mostly have more awareness about the social networks. If they have less awareness based on their usage, the general report is sent to the end users.

The social network questionnaire survey score card data has been collected for the 31 to 35 age group.

The rating has been fixed based upon the score card attained by users in this age group. If the user has a low score about social networking issues then the report will upgrade them with the advantages and disadvantages of social networks.

Eligibility of adult age grouper =

If the score card = 8.6/10 to 10/10 -> High awareness If the score card = 6/10 to 8.5/10 -> medium awareness If the score card = less than 5.9/10 -> Low awareness

The above age categorical analysis has been tested before using the UAN system. After the age categorization verification, the system will next evaluate the user view analysis

 
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