Thursday, December 12, 2019
Red Bull Tweet Analysis for Non-Alcoholic- myassignmenthelp
Question: Discuss about theRed Bull Tweet Analysis for Non-Alcoholic. Answer: Introduction Red bull a well-known energy drink producer is mainly popular among young adults. Being a non-alcoholic beverage manufacturer Red bull made its position among the top energy drink manufacturer. Within very less period of time Red bull made its way becoming among the tops (Tang et al., 2014). On the other hand, throughout this ten year of span technological advancement also provided various aspects and created different new mediums or platforms for promotion of one product or service. On the internet that is the backbone of the social media handles e.g. Facebook, Instagram and Twitter is the current channel for different businesses to promotion of their services unlike the previous times when the television was the main media of information (Shendge et al., 2015). The social media handles helped the brands to find and reach to its desired customer through interactions on those platforms. In addition, consumers became able to share their feelings and feedbacks using the social media pl atform posts that are generally 140-character text in twitter while facebook status and instagram posts also available for the same purposes. The more social media saw growth in its customer database the more advertisers approach those platforms to monetise their contents for getting the benefits of the service along with promotion of own products (Sakaki, Okazaki Matsuo, 2013). A popular brand like red bull does not always require promoting its soft drinks while it also promotes other purposes too like sports and musics. Objective Like many of the product manufacturer Red Bull have also its presence over all the major social networking platforms. The objective of this study is analysing a collection of tweets that were made on the twitter handle of the organisation (Han et al., 2017). The entire energy drink manufacturers always promotes that their products boosts physical and mental performance. However, over consumption of the drinks may lead to serious health issues that the energy drink manufacturer ignores (Da Silva, Hruschka Hruschka, 2014). In this report, the tweets collected from the official handle of the organisation is analysed and reviewed to figure out whether the organisation Red Bull takes neutral steps for promotion or it heavily promotes its consumption. Literature Review According to Bruns and Stieglitz (2015), the research conducted by them on the tweets as a main source of information. Taking into concern the research conducted by Oh, Sasser, and Almahmoud (2015), they had analyzed the super bowl advertisements for their source of information. It has been found critical to use the tweets for the basis of information for the research helps the researchers to obtain information from real time events (Baralis et al., 2013). They are all real events occurring around the world. Analysis of the tweets help the researchers to gain critical evaluate able information on the organization they are conducting the research on, specifically the marketing department of the organization. Burghardt (2015) has found different tools that are helpful for the usage of analysis of the tweets collected form organizations twitter feed. The scholar further reveals that the data obtained from the tweet analysis can be easily done using the Restful APIs. The tool has been ma de available for the researchers and the practitioners for their data research. To understand the tools that can be used for the analysis tools to be used on the tweet feed collected, researchers have found publications that have used similar working on tweets. From the documents read it has been found that Kharche, and Bijole (2015) has provided different techniques that can be used by the researchers to analyses the tweets. The authors have specifically established different algorithms and methods to analyses the tweets collected. This has been collected form a publication from Shrivatava, Mayor and Pant (2014) where the researchers have used tools for the textual analysis of tweets. Finally a publication from Kiruthika, Woona, and Giri (2016) showed the analysis of tweets using a freely available software R. Methodology Data Collection and Variables The latest 100 tweets including retweets are taken from the official twitter page of Red Bull. Those tweets are collected and recorded as an excel file for analysis purposes (Ceron et al., 2014). The analysis of the tweets are broken in four parts to evaluate Analysing the type of the tweet whether it is posted for commercial purposes or not. Analysing whether, the tweets promote consumption of energy drinks directly or it leaves behind any ideas. Analysing whether, it promotes itself through various phrases or the hash tags. Analysing whether the brand associating with other brands that reflects on the tweets. These are the variables of the study. In addition, there are two others variables that are also used. However, those does not reflects much in this scenario. Those are: The number of tweets that are posted along with emojis. The number of tweets contains an URL or link that redirects to some other vendor. During the collection of raw tweets, There were some tweets that were made in non-English languages, those are eliminated from the tweet collection for better understanding of the case. Findings Post the collection of the tweets all the tweets are manually studied, and verified to categorise those tweets. Within those 100 tweets, Red Bull directly posted half of those and the rest were retweets or tweets from others that the Red Bull re-tweeted (Chaurasia et al., 2016). Finding out which category the tweet belongs to the above mentioned criteria. An example is given below: Figure 1: A tweet from Red Bull (Source: twitter.com/redbull) The above tweet clearly promotes drinking of Red Bull where the brand is promoting itself. After analysing, the entire tweet the analysis records the outcomes as shown below: Type of tweet Related to red bull Includes URLs Includes Emojis Pro Energy drink Cessation Message Commercial 21 9 13 11 0 Non-Commercial 79 16 32 13 7 Total 100 25 45 24 7 Types of Promotion Total No. of Tweets Events 17 Incentives, Offers 12 Celebrity Endorsements 30 Conclusion The research evaluated that among the 100 different tweets 79 or maximum of the tweets are non-commercial while only 21 tweets are commercial. A big organisation like Red Bull puts less effort to promotes itself while it keep posting regarding other sponsored events and celebrity endorsements. Those of the non-commercial tweets contain the most URLs that are not related to the Red Bull. It is also found that the non-commercial messages mostly promote drinking energy drinks while the organisation itself puts less effort encouraging consumers. This research also helped to come across other soft drink producer that attracts young peoples with promotion of new products made of less sugar and harmful ingredients. However, because of the maximum presence of the consumers over the social media helps the organisation to be online for gaining more trust in the process of being a household name. References Baralis, E., Cerquitelli, T., Chiusano, S., Grimaudo, L., Xiao, X. (2013, September). Analysis of twitter data using a multiple-level clustering strategy. InInternational Conference on Model and Data Engineering(pp. 13-24). Springer, Berlin, Heidelberg. Ceron, A., Curini, L., Iacus, S. M., Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens political preferences with an application to Italy and France.New Media Society,16(2), 340-358. Chaurasia, C., Shreshta, B., Patel, H., Kesharwal, V., Vikrant, B. M. (2016). A Survey on Tweet Analysis and Real Time Detection for H1N1 Virus Reporting System.International Journal,4(3). Da Silva, N. F., Hruschka, E. R., Hruschka, E. R. (2014). Tweet sentiment analysis with classifier ensembles.Decision Support Systems,66, 170-179. Han, Y., Hong, B., Lee, H., Kim, K. (2017). How do we Tweet? The Comparative Analysis of Twitter Usage by Message Types, Devices, and Sources.The Journal of Social Media in Society,6(1), 189-219. Sakaki, T., Okazaki, M., Matsuo, Y. (2013). Tweet analysis for real-time event detection and earthquake reporting system development.IEEE Transactions on Knowledge and Data Engineering,25(4), 919-931. Shendge, G. V., Pawar, M. R., Patil, N. D., Pawar, P. R., Bagul, D. B. (2015). Real time Tweet analysis for event detection reporting system for Earthquake.IRJET: International Research Journal of Engineering and Technology,2(6), 706-712. Tang, J., Meng, Z., Nguyen, X., Mei, Q., Zhang, M. (2014, January). Understanding the limiting factors of topic modeling via posterior contraction analysis. InInternational Conference on Machine Learning(pp. 190-198).
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