Resepsi Penonton tentang Tayangan Pernikahan Pada Kanal Youtube (Studi pada Mahasiswa Universitas Muhammadiyah Malang Penonton Tayangan “Nikah Muda, Cerai Muda”)

The internet continues to evolve, giving rise to new media, with Youtube being a popular choice among Indonesians. In the creative industry, Youtube presents significant opportunities, leading companies to create channels aimed at capturing audience attention. The Youtube channel "Menjadi Manusia" is a successful production house that has garnered much sympathy from viewers through its program "Berbagi Perspektif," focusing on young marriages. Varied audience perceptions in the Youtube comment section indicate a genuine interest in the topic of young marriages. This aligns with Stuart Hall's reception analysis theory, emphasizing that each audience member interprets the meaning of a message differently. This study focuses on understanding how University of Muhammadiyah Malang Students interpret the content of the show titled "Nikah Muda, Cerai Muda" (Young Marriage, Young Divorce). This research employs a qualitative approach within a constructivist paradigm. Qualitative methods are utilized to describe and analyze individual or group events and thoughts. Primary data for this study is derived from transcripts of participant Focus Group Discussions (FGD). The findings reveal varying interpretations of the message. Among the eight participants, a dominant hegemonic position agrees with the message conveyed by the Youtube channel "Menjadi Manusia," emphasizing that marriage is not solely about mental and financial readiness. In a negotiated position, three participants largely agree with "Menjadi Manusia" but suggest the inclusion of successful married individuals to alleviate post-watching concerns and fears, while enhancing insights into young marriage. No opposing data rejecting the content of the Youtube show was found

You tube spam comment detection using support vector machine and k–nearest neighbor

Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problem

In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale

How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner --- with the objective of searching for individuals that have similar pattern of behavior as the known seeds --- based on a graph diffusion process via local spectral subspace. We offer a fast, scalable MapReduce deployment adapted from the localized spectral clustering algorithm. We demonstrate the effectiveness of our deployment at Google by achieving an manual review accuracy of 98% on YouTube Comments graph in practice. Comparing with the state-of-the-art algorithm CopyCatch, Leas achieves 10 times faster running time. Leas is actively in use at Google, searching for daily deceptive practices on YouTube's engagement graph spanning over a billion users.Comment: accepted to the International Conference on World Wide Web (WWW'16