![]() ![]() In this paper, we investigate similar-context trending hashtags to characterize general behavior of specific-trend and generic-trend within same context. Understanding the posting behavior patterns as the information flows increase by rapid events can help in predicting future events or detection manipulation. Twitter trending hashtags have been one of the topics for researcher to study and analyze. Twitter is a popular social networking platform that is widely used in discussing and spreading information on global events. The repost networks of unsuccessful hashtags exhibit a simple evolution pattern. The former is usually dominated by a super-hub and the latter results from consecutive waves of contributions of smaller hubs. The evolution of the repost networks of successful hashtags before getting to the HSL show two types of growth patterns: "smooth" and "stepwise". When analyzing this time we distinguish two extreme categories: a) "Born in Rome", which means hashtags are mostly first created by super-hubs or reach super-hubs at an early stage during their propagation and thus gain immediate wide attention from the broad public, and b) "Sleeping Beauty", meaning the hashtags gain little attention at the beginning and reach system-wide popularity after a considerable time lag. We have found that the circadian activity pattern has an impact on the time needed to get to the HSL. We investigate the prehistory of successful hashtags from 17 July 2020 to 17 September 2020 by mapping out the related interaction network preceding the selection to HSL. To understand the emergence of hashtag popularity in online social networking complex systems, we study the largest Chinese microblogging site Sina Weibo, which has a Hot Search List (HSL) showing in real time the ranking of the 50 most popular hashtags based on search activity. English and Arabic languages comprised close to 40% and 20% of the first rank topics, respectively. More than 50% of the topics could not hold the position for more than an hour. Based on our results, 77.6% of the topics that reached the Top-10 list were trending with less than 100k tweets. We propose and analyze our dataset according to six criteria: lexical analysis, time to reach, trend reoccurrence, trending time, tweets count, and language analysis. To this end, we automatically accessed Twitter’s trends API and stored the resulting 50 top trending topics in a novel dataset. In this article, we thoroughly examined the Twitter’s trending topics of 2018. Nevertheless, there have been very few works focused on the dynamics of these trending topics. Twitter trends list has a powerful ability to promote public events such as natural events, political scandals, market changes and other types of breaking news. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a "trending topic" or simply “trend”.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |