I tracked Twitter trending topics of 11 countries and worldwide region four times daily for 45 days from February to April 2022. My findings suggest that entertainment topics dominate the trending topics. The popularity of trending topics doesn’t reflect their real impact on the society. Keyword occurence patterns vary between countries. In all countries, the keywords occured sparsely. Some countries have more continuous patterns than others. Trending topics can be authentic, i.e. formed by natural conversations, or inauthentic, i.e. formed by bots or political campaign teams. Thus far, the trending topics patterns didn’t seem to reflect the authenticity of the conversations.
My other takeaway is that patterns observed in my current trending topics monitor was not enough to determine the authenticity of a trending topic. Abnormally high volume coupled with consistency in temporal and regional dimension may increase the likelihood that such trending topic keyword is authentic. Further processes such as context examination, topic labeling of keywords, or filtering out of ‘noise’ keywords may be required to produce more useful and credible insights. Having said that, also after observing the trends with their diversity and dynamics, I think that any claim that a certain topic is people’s aspiration based only certain keyword’s existence in the trending topics should be examined closely for its credibility. Tracking dynamics of individual keywords may yield more useful insights regarding people’s aspirations, rather than indiscriminately monitoring trending topics.
Twitter trending topics of from the countries United States, Indonesia, United Kingdom, Australia, Singapore, Russia, Japan, South, Korea, India, France, Germany, and area Worldwide were tracked from February 28, 2022 to April 14, 2022. The data was pulled roughly 7 times every day. To explore the resulting dasboard and view its more detailed information, please see Twitter Trending Topic Monitor.
As previously mentioned, trending topics pertaining to entertainment dominate the data. The entertainment part mostly pertained to K-Pop. K-Pop is visibly dominant in Indonesia, India, France, South Korea, Singapore.
There were three major events that happened during the monitoring that affected the trends, namely the Academy Awards ceremony or Oscars, particularly the event of Will Smith slapping Chris Rock, the Ukraine – Russia war, and The Grammy Awards. The topic that dominated the overall data seems to be the Oscars. Its consistent high volume appearance in all areas in the span of several days seems to indicate that the conversations about it were authentic. The Grammy’s topic appeared less pronounced than Oscars and it seemed to be promoted by the appearance of BTS (a K-Pop group) there. The Ukraine – Russia war topic appeared in high volume in several countries, but it is far less popular than the entertainment topics. Based on this fact and the fact that the war has far more serious impact of more people’s lives, it can be suggested that the trending topics popularity doesn’t reflect their real impact the society.
Most of the trending topics were not continuous in terms of time, e.g. what’s popular in today is most likely not as popular tomorrow. Thus, the time series graph of the trends appear sparse. This pattern is similar to the Google Trends trending search pattern. Interestingly, some countries such as South Korea, Russia, and Singapore have more visibly continuous pattern that the others. Some of the continuous topics were entertainment topics, some are about politics. So, so far I don’t have any further clue as to why the patterns varied.
The appearance of high volume outlier trending topic keywords that skewed the time series graph upwards was common for all regions. Such outliers may or may not pertained to celebrity figures. About the outliers that pertain to celebrities or idols, considering that it is commonly known that celebrity fans often mobilize themselves to inflate popularity of their keywords of interest and often using duplicate user accounts, filtering out of such keywords can be an option to improve the insight discovery process. Political groups and groups with power can also inflate the popularity of keywords. For this kind of inflation, I think the current dashboard cannot be used to identify its patterns. Probably it would take a more frequent update of the data, e.g. hourly update (the current dashboard updates about every 3 hour), to get more discernible patterns. Identification of such political keywords inflation may also be conducted by investigating the users that tweeted them or investigating the context of the keywords.
