Abstract: Today, many news sites let users write comments on news articles, rate others' comments by upvoting and downvoting, and order the comments by the rating. Top-rated comments are placed right below the news article and read widely, reaching a large audience and wielding great influence. As their importance grew, upvotes and downvotes are increasingly manipulated by coordinated efforts in order to push certain comments to the top. Boosted comments are often well-written, but they represent biased opinions, making the manipulated consensus to be seen as public opinion.In this paper, we analyze comment sections of articles targeted by coordinated efforts and identify a trace of vote manipulation. Based on the findings, we propose a parameterized classifier that distinguishes comment threads affected by coordinated voting. Using the classifier and our choice of parameters, we have examined six years of the entire commenting history on a leading news portal in South Korea. Manual inspection with side-channel information could only identify hundreds of targeted articles. With our classifier, we have identified more than ten thousands of comment threads with a high likelihood of manipulation. We report that this type of coordinated manipulation increased significantly in recent years.