Research on the Third-Person Effect of Online Commercial Advertisements - Based on the Students from Guangzhou Huashang College

The third-person effect hypothesis has become one of the most important aspects in the research field within the American empirical school.A large number of studies have adopted empirical research methods to verify the reliability of the third-person effect.With the rise of the network society,local research on the third-person effect has gradually extended to the verification or falsification of the third-person effect in the network environment.This article begins with a study on the third-person effect of online commercial advertisements based on the students from Guangzhou Huashang College.Through the study,the research hypotheses have been proposed and questionnaires have been distributed to the research subjects for analysis.Based on a series of quantitative operations,such as data analysis,empirical observations,and empirical research,this study provides a source of reference and reflection for research in this field.

Machine Learning-Based Advertisement Banner Identification Technique for Effective Piracy Website Detection Process

In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement.Billions of dollars are lost annually because of this illegal act. The currentmost effective trend to tackle this problem is believed to be blocking thosewebsites, particularly through affiliated government bodies. To do so, aneffective detection mechanism is a necessary first step. Some researchers haveused various approaches to analyze the possible common features of suspectedpiracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In addition, theseadvertisements have some common attributes that make them unique ascompared to advertisements posted on normal or legitimate websites. Theyusually encompass keywords such as click-words (words that redirect to installmalicious software) and frequently used words in illegal gambling, illegal sexual acts, and so on. This makes them ideal to be used as one of the key featuresin the process of successfully detecting websites involved in the act of copyrightinfringement. Research has been conducted to identify advertisements servedon suspected piracy websites. However, these studies use a static approachthat relies mainly on manual scanning for the aforementioned keywords. Thisbrings with it some limitations, particularly in coping with the dynamic andever-changing behavior of advertisements posted on these websites. Therefore,we propose a technique that can continuously fine-tune itself and is intelligentenough to effectively identify advertisement (Ad) banners extracted fromsuspected piracy websites. We have done this by leveraging the power ofmachine learning algorithms, particularly the support vector machine with theword2vec word-embedding model. After applying the proposed technique to1015 Ad banners collected from 98 suspected piracy websites and 90 normal orlegitimate websites, we were able to successfully identify Ad banners extractedfrom suspected piracy websites with an accuracy of 97%. We present thistechnique with the hope that it will be a useful tool for various effective piracywebsite detection approaches. To our knowledge, this is the first approachthat uses machine learning to identify Ad banners served on suspected piracywebsites.