Content Analysis of Social Media on Indonesia Vaccination Covid-19 Policy

The purpose of this study is to determine the types of social media platforms used by proponents and opponents of vaccination. This research employs a qualitative approach, analyzing social media hashtag data with Q-DAS (Qualitative Data Analysis Software) and Nvivo 12Plus. This study finds that: First, social media was used to spread both sides' narratives and content. Second, social media relation tends to be quite strong, but the pro-side is stronger than the contra-side. Third, the narration on both sides uses hashtags and a single word to spread the influence.  They were used for vaccination issues on two sides of the issue.  This research limitation, like this study concentrating exclusively on social media data, excluded digital data like the phenomenon on social media only. The recommendation for the following research is: Try to understand the social movements opposing or promoting vaccination in Indonesia and compare them to other Asian countries.Keywords: vaccine; policy; social media

Fast and Secure Friend Recommendation in Online Social Networks

Online Social Networks have completely transformed communication in the world of social networks. Participation in online social networks have been growing significantly and is expected to continue to grow in the upcoming years. As user participation in online social media is on the rise, so is the concern pertaining to user privacy and information security; users want to interact on social media without jeopardizing their privacy and personal information. Extensive research has been conducted in the area of developing privacy-preserving protocols to allow users to interact in a secure and privacy-preserving environment. One of the elements that social media have is the feature or ability to befriend other users. While a user may manually search for friends to “add”, social media networks like Twitter, Facebook, Instagram, Snapchat and others facilitate friend recommendations to their users based on different criteria. We examine and compare the advantages and disadvantages of existing privacy-preserving techniques and schemes. We also analyze di↵erent models used to implement friend recommendation protocols and study proximity measurement metrics used in existing works. This thesis scrutinizes the security weaknesses and vulnerabilities of three Friend Recommendation Protocols from existing work and develop a corresponding solution. We propose a (FSFR) protocol that is based on Shamir’s Secret Sharing to facilitate friend recommendations in Online Social Networks in a fast, secure and private manner. After comparing our protocol with existing protocols in terms of security, computation efficiency, costs, flexibility and more, we conclude that our FSFR protocol guarantees a superior and more efficient friend recommendation protocol

Recommendations in Social Media Applications to Ensure Personification and Safety using Machine Learning

Myriads of social media utilization lead to various issues like personalization hacks, data security problems, and safety. A recommendation is of paramount importance to alleviate this problem when there is a huge amount of data and the number of participants on the platform is increasing exponentially. Unfortunately, modern social media research has enhanced the performance and personalization of recommendations in many fields, yet largely underutilizes the power of artificial intelligence to enable personalized recommendations system for social media platforms like WhatsApp, Facebook, Twitter, etc. With advancement inside the global of technology every hour and every day new features are delivered to the list.  In a manner, social platforms are merging into our actual existence, and to achieve personification and related safety, users can get any one safety factor from all 6 classes with this approach. This factor provides the basis for personification and the implementation of safety precautions. This research proposes recommendations for personification in social media applications. The proposed Modified Inception Resnet V4 Convolutional Neural Network (MInReCNN) outperforms embedded media persona analysis and classification through text, image, and video data. Using these prediction classes better decisions can be made in given social media domain

FRECOMTWEET: PRODUCT RECOMMENDATION APPLICATION USING FRIENDSHIP CLOSENESS ON TWITTER

The information and communication technology development makes someone interact with each other easier. This convenience is used to exchange ideas, like using social media Twitter for product recommendations before buying it. It brings up a trend that consumers seek product recommendations through other people on social media. Social media, especially Twitter, has several features such as tweets, ReTweet and mentions to interact with other people. Users can describe the product, attach a link, and give a positive or negative rating in a tweet. These types of tweets can be used as an alternative to product recommendations. FrecomTweet is an Android-based product recommendation application that can detect close friendships based on the user’s ReTweet and mentions. This application also detects a product recommendation that appears in a conversation between users. This detection uses the keyword filtering method, which matches the conversation content with the markers in the database. If the conversation has a positive rating, it will recommend the user’s closest friends. This research uses a crawling method with the Twitter API streaming filter built using the CodeIgniter framework. The results of the black box test show that Twitter user conversations can be used as a product recommendation with a precision and recall value of 0.94 and 0.81, respectively

Package Tour Recommendation System using Decision Tree Algorithm

The global market size of online travel agencies was 470 billion US dollars. Basically, travel agencies are companies that are selling travel related services like tourist activities, accommodation, transportation services etc. to the public. Package tour is also a service provided by travel agencies to their customers. Package tour for a particular destination is best option for an individual or a group of people who wants to hassle free trip. But list of travel agencies is increasing day by day and choosing a best travel agency for a particular trip is really a difficult job. Machine Learning algorithm can solve this problem with the help of Recommendation System. In this paper I am going to design a Package Tour Recommendation System using Decision Tree Algorithm that receive tourist request for package tour, analyse it and recommend top three travel agencies. Decision Tree algorithm is one of the most popular and effective machine learning algorithms for recommendation system. In today’s world Recommendation system enters in almost every field like entertainment, social media, e-commerce, advertisement industry etc.  &nbsp

An Experiment of Game Promotion and Selling Using Twitter

— The combination of the internet, social media and mobile phones makes the social mobile game is becoming a huge market with high growth rates from year to year. This trend is attract the game developers/publisher vying to enter this game market including in Indonesia. In other hand, Twitter as one of social media has a major influence on consumer purchase decisions especially in social mobile games. Consumer seeking recommendation about game that they want to download based on their friend recommendation and content that their consume in social media before visit online store. As for Indonesia game developers most of their marketing activities were more to game gathering or events, there is little that effectively use social media as marketing channel. Social media adoption including twitter in Indonesia game developer is at stage of connectivity and proff of company existance. The purpose of this research is to know does using twitter as social media marketing have effect to influence consumer and download mobile game. In this research, experiment methodology was employed. Experiment was choosed because to have real insight about the effect of twitter as social media marketing in building games relationship with consumer and increase the number of game download. Stack The Stuff, game from PT. Nightspade was choosed as research object. The implementation using OASIS frawework as guidance. The results from the experiments in this research measured using Social Model Exposure-Engagement-Influence-Action from Don Bartholomew.Twitter as media marketing executed by carrying experiment 1 (15 August 2012 - 15 September 2012) with buzzing methods first, after it finish, followed by experiment 2 (22 September - 22 October 2012) with tweeting and offering method. Then, both experiment results compared to know which the better Twitter marketing method. The measurement using several tools, namely TweetLevel, Sprout Social, and downloads data. With confidence level 95%, our results suggested that twitter as media marketing with buzzing method have effect to increase game download and tweeting and offering method have effect to increase product engagement and influence in Twitter. Furthermore, in the end of research, there are recommendations to implement twitter as social media marketing for small-middle sized company like Indonesia game developer

Improving the Performance of Recommendation on Social Network by Investigating Interactions of Trust and Interest Similarity

On the social media, lots of people share their experiences through various factors like blogs, online ratings, reviews, online polling and tweets. Study shows that the factors such as interpersonal interest and interpersonal influence from the social media which is based on the circles as well as groups of friends leads to opportunities and challenges in solving the problems on datasets. This challenge is for the Recommender System (RS) to find the solution on cold start and sparsity problems. In this paper, on the basis of the probabilistic matrix factorization, the social factors like personal interest, interpersonal influence and interpersonal interest similarity are combined into a unified personalized recommendation model. These factors can improve the associating linkage in latent space. Various datasets are used to conduct the experiments to get the results that show that the proposed model performs better than the existing approaches