Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time.
Research related schema matching has been conducted since last decade. Few approach related schema matching has been conducted with various methods such as neuron network, feature selection, constrain based, instance based, linguistic, and so on. Some field used schema matching as basic model such as e-commerce, e-business and data warehousing. Implementation of linguistic approach itself has been used a long time with various problem such as to calculated entity similarity values in two or more schemas. The purpose of this paper was to provide an overview of previous studies related to the implementation of the linguistic approach in the schema matching and finding gap for the development of existing methods. Futhermore, this paper focused on measurement of similarity in linguistic approach in schema matching.
Website has evolved since it was first developed in 1990. Since then, the website grows rapidly. Based on the information provided by http://www.worldwidewebsize.com the number of websites is currently at least 4.54 billion pages. With a very large number, the website stores a lot of information that can be used. That problem brings up the concept of data extraction. Web data extraction aims to retrieve the contents of the website so that it can be easy to use for other purposes. The utilization of web data extraction can be used in a product catalog, news, bookstore, travel, etc. There are many systems build by different technique such as manual, supervised, un-supervised, and semi-supervised. This paper discuss supervised learning technique for web data extraction. Several previous surveys have overviewed the wrapper induction system using the concept of supervised techniques to extracted web data up to 2007.
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