SOCIAL MEDIA ENGAGEMENT “RUSSIAN AND UKRAINE NEWS” POST AGGRESSIVE FIRST TRENDING ON TWITTER
Media Observation Through The Social Network Analysis Method
On this occasion, the author and researcher are currently actualizing the results of independent learning regarding whether a social media can be used for research media that is comprehensively related to the participation aspect of Twitter social media users as an audience as well as informants on the development of information that is present to be accepted by other audiences. Through this opportunity, allow the author to present the results of brief research on how a very complex social media dynamic can be analyzed very practically using big data taken through the Drone Emprit Academic (DEA). First of all, the author refers to the limitations of the exposure of the research results, the author does not provide criticism of the phenomena that occur and does not provide correct arguments regarding the dynamic phenomenon of the information that occurs.
When we talk about big data projections the imagination that arises in us is maybe about some things related to an identity of a calculated data and some things that may be quite specific maybe concerning all micro information, and some things related to technology and information. everything we understand about big data is true, but the thing that may come to our mind is how do we get that data massively considering the amount of information that must be absorbed and received and must go through a calculation process in simplification related to the data obtained. To answer this, Social Network Analysis in today’s modern era can provide factual data regarding the number and sentiment analysis data related to the phenomenon or trend of an issue to be studied, for example, global trends regarding #happynewyear2022 or about #Trumpforpresident politics which are busy. Twitter users participate in tweets. Through these hashtags, social media users, especially Twitter, must have a connection with the creation of a hashtag that provides collective information and a phenomenon that is considered familiar to the information produced, through this example of application we may have questions regarding whether a piece of media information can be analyzed both in terms of tendencies, participation, and other data. To answer this question, let’s analyze small research that the author has done in the last 1 month since this article was written.
The data obtained was started and collected from April 18 to May 17, and automatically big data obtained the data, a period of 1 month is a suitable period to analyze whether the participation of Twitter media users is still very active in engagement after the initial information explosion of the Russian war and Ukraine happened.
The UI/UX displayed by the Drone Emprit Academic is more or less listed in the attached image above, to conduct research on a news media or aspect that will be crawled data first, fill in several keywords and categories to be analyzed, first, the researchers do a big theme in the form of what major themes will be researched, for now, the major themes to be researched are the Ukraine and Russia conflicts, then a sub-configuration about project naming will appear, the researcher uses Indonesian in the sentence Russian and Ukrainian conflicts for project naming, then the subject to be researched is specific on keywords because considering the phenomenon of reporting on the Ukraine and Russia wars is not dominated by one Twitter account in its reporting, then the data used is a qualitative mix method to determine the tendency of opinion on Twitter and quantitative to determine the amount and form of participation in conducting social media activities. engagement connection between user and media. The inserted keyword is in Indonesian “Conflict, Ukraine, Russia war, Zelenskyy, Putin.” The determination of keyword diction will greatly affect the crawling of data that will be used for the next analysis, then the media type uses Twitter because the mobility of information and user engagement is very massive and dynamic, very suitable for data crawl is performed.
Scalping Data Twitter
In the timeline, through the Drone Emprit Graph platform, it is shown that it tends not to be high in Indonesia, even showing below 100 forms of engagement that occurred on several dates that the researchers did. Through these mentions, it can be analyzed that the production of information received by the Indonesian audience tends not to stagnate, it can even be said to be seasonal after the explosion of information production about the Ukraine war.
The overall platform used is social media Twitter because through the assumption of researchers that social media is one of the communication platforms with a very high level of mobility and easy to use, Twitter users are more likely to show their existence in analyzing the production of information in text form.
SNA. Social network analysis is a form of mapping the engagement that occurs in the production of information, the information that is meant is how the role of the media or user in doing engagement with a specific purpose uses one of the empirical phenomena that occurs to provide news that is relevant to other audiences, Through this empirical phenomenon, the production of information about the Ukraine and Russia wars should be able to be analyzed because at the beginning of the information explosion period, it was not only on a national and even international scale. On April 1, 2022, researchers researched social network analysis, what happened after the information explosion on the Twitter platform, through this, several forms of engagement by Twitter users were at a dynamic level that was not too dynamic, this was due to the number of participants of each user on an issue. or the information provided tends not to be massive because the polarization that occurs in social network analysis generally occurs due to a large number of user participation and the quantity of engagement that occurs in a published information phenomenon. there are only 4 users who have more influence in carrying out engagement on each user and some of the engagements carried out are also not relevant to aspects of empirical phenomena that occur especially in the aspects of the Russian and Ukrainian wars, each user does not have the same information engagement linkage and tends to create your circle of communication. The conclusion obtained from the results of this study is that the scale of engagement carried out after the Ukraine and Russia wars tend not to be massive because Twitter users may empirically discover a new phenomenon in an issue on Twitter.
