Twitter Visualization Research Methodology - Assignment Example

Twitter Visualization Table of Contents Introduction 3 Research 3 Analysis 4 Conclusion 6 References 8 Appendix 9 Introduction In the present scenario, communicating through “Social Networking Sites” has become a common phenomenon. Technological innovation has changed the life style of every human being. The world of the internet and its different sites has been making the life of a human being more extended and easier. The social networking sites such as “Twitter” and “Facebook” have captured a particular space of human being. Nowadays, people of different age groups are regularly involved in accessing the social networking sites. The social networking sites have provided cost-efficient ways of communication for people. Facebook and Twitter both sites are dissimilar from each other, and people of different age groups have been involved in the access of these sites for various purposes. Contextually, Twitter is a site where people of different background shared their views about different topic. In this regards, Twitter has been used by the corporate for better accessed to its employees. Nowadays, “Facebook” and “Twitter” is the new source for researchers. Students of masters levels have found “Facebook” and “Twitter” as an interesting topic for their research (Bryman, 2012). Against this backdrop, this study intends to develop a comparison of data from twitter to see the tweets in all Australian states in the morning and evening one hour 9-10 am and 5-6 pm. In order to accomplish the aims and objectives of the study, the analysis of data pertaining to the tweets made by the people in different States of Australia is being conducted. Research To prepare a report on a particular subject area, a large volume of data is required. The availability of required data helps to form the report correctly and accurately. In other words, the availability of accurate and precise data is essential for ensuring the reliability of any research study. In this context, to develop an accurate report demonstrating the critical comparison of data from Twitter to determine the tweets in all Australian states in the morning and evening one hour 9-10 am and 5-6 pm the frequency and rates of tweets made by the people living in these states are collected. At the same time, the data for the study also involves the tweets made by the people in the different states of Australia using hash tag and non-hash tag. Accordingly, it has been observed that time is the most precious element for every individual as once elapses, it never comes back. On weekday and weekend, people follow different kind of schedules and performed a different set of tasks according to their routine. Notably, the comparison of data is made on the basis of tweets made on the weekend and the weekday. Notably the data were compiled from the states of Australian Capital Territory (ACT), Northern Territory (NT), Western Australia (WT), South Australia New South Wales (NSW), Queensland (QLD) and Victoria (VIC). Social network mining is an important as well as interesting topic of human being. Due to high attraction towards the virtual medium, corporate professionals gives priority to the social networking sites Twitter is one of them (Russell, 2011). Due to technological intervention and the globalization current working pattern of every industry has changed, every industry has its own site through which the consumers can interact with the authority instantly and give suggestions for the betterment of the company. The social networking sites have created a new platform for the companies and its consumers, where both parties can easily interact with each other. Nowadays, companies have been using Twitter groups for tracking meetings and attendance of the meeting (Krishnaswamy & et. al. 2009; Kothari, 2004). Analysis The objective of the assignment has been to interpret twitter data pertaining to the tweets made by the people in different states in Australia on the weekend and the weekday. The different charts and diagrams are used for this interpretation. Social networking is a part of every human being. Updating status and tweeted about current trending news is the new emerging tendency of people. Contextually, the assignment is based on the twitter visualization. The data are been based on different States of Australia based on the frequency of tweets made on weekday and weekend. The comparison of data revealed that on the weekday, people in Australian Capital Territory (ACT) was involved tweeting mostly on a subject relevant to TV while no tweets were made on the music. In this regard, the 30% tweets were made on the subject relevant to TV while 0% was identified with respect to music on the weekday in ACT. Similar rates of tweets, i.e. 20% each were determined with respect to the subject relevant to Place and other miscellaneous areas. In the similar context, tweets relevant to government, sport and occasions were also reported to have a similar figure of tweets on the week day in the ACT. At the same time, 40% tweets made on the weekday in ACT were determined to be made using hash tag while a maximum of 60% tweets were made without the use of hash tags. As far as the tweets made on the weekend are concerned, it was determined that maximum tweet of 30% was made on topic relevant to sport while none of the tweets were made on the topic occasion and music. Tweets on the topic relevant to Tv, government and Place were determined to share an equal percentage of 20% each on the weekend in ACT while 10% tweets were made on the topic other than the undertaken variables of topics for the research. A similar percentage of tweets on the weekend to that of the weekday were determined with respect to the use of hash tag and non-hash tag tweets (refer to appendix fig.1 and fig:2). In the state of South Australia (SA) maximum of 40% tweets were made on sports while no tweets were made on occasion on the weekday. Equal percentages of tweets were made using hash tag and non-hash tag on the weekday in SA. On the other hand, 78% of tweets were made on sports on the weekend in SA while 10% each were reported with respect to TV and government. Similar to that of the weekday, tweets made using hash tag and non-hash tag on the weekend in SA were also observed to share equal percentages(refer to Appendix fig. 3 and fig:4 ). With respect to the tweets made on the weekday in New South Wales (NSW), it was determined that maximum of 50% tweets were made on TV while no tweets were made on Place and occasion. Notably, the 60% tweets were determined to be made using hash tag while 40 % were identified to be made without the hash tag on the weekday in NSW. At the same time, 90% tweets were determined to be made on sports on the weekend in NSW while 10% on sports were determined. None of the tweets were ascertained to be made on another topic on the weekend in NSW. At the same time, the 60% tweets were made using non-hash tag while 40% tweets were reckoned to be made using hash tag(refer to appendix fig.5 and fig:6). The data pertaining to the tweets made on the weekday in Queensland (QLD) revealed that 40% tweets were made on topics relevant to Tv while 10% on music and occasion and 20% on place. None tweets were identified on sport. Equal percentages of tweets were reclined with respect to tweets made using hash tag and non-hash tag. On the other hand, 70% tweets were made on sport on the weekend in QLD and 10% each on place, Tv and music. In addition, 40% tweets were made using hash tag while 60% were determined without the hash tag on the weekday in QLD(refer to appendix fig: 7 and fix: 8). In the state of Victory (VIC), 45% tweets were determined to come for sports and 0% on government, occasion, and misc. Similarly, 33 percent tweets were determined lodged for Tv, and 11% tweets on place and music In the weekend, people have been determined to tweet 80% on sports and 0 percentage on Tv, government, occasion, and miscellaneous while 10% on music and place in VIC on the weekend. In addition, it was found that 30% tweets were made using hash tag in VIC both on the weekday and weekend while 70% tweets without the hash tag were determined in both the cases (refer to appendix fig: 9 and fig: 10) In Western Australia (WA), it has been seen that 40 percent tweet was made on place and 0 % tweet on music on the weekday. Only 10% tweets was determine to be made using hash tag in WA on the weekday while 90% tweets were made without the use of the hash tag in WA on the weekday. On the other hand 60% tweets were made using hash tag on weekend in WA while 40 were estimated without the use of the hash tag(refer to appendix fig: 11 and fig: 12). The people in Northern Territory (NT) were determined to tweet 70 % on place and 30% on topics other hand the included topic while 0% tweets were determined on government, Tv, occasion, sport and music on the weekday. Out of 100 percent, 50% tweets were identified to lodge on place and 0%tweet on music, occasion, Tv, and government on the weekend in NW. In addition, equal percentages of tweets were determined to made using hash tag and non-hash tag on the weekday and the weekend in NW (refer to appendix fig: 13 and fig: 14). The aggregate percentage pertaining to the tweets made on different topics across the states under study revealed that maximum tweets were made on the topic place which was reckoned to be 30%, followed by tweets on sports and TV with 23% each respectively on the weekday. On the other hand, the combination of the data from the states undertaken for the study demonstrated that 54% of tweets were made on sports while 30% on place on the weekend (refer to appendix fig: 15 and fig: 16). Similarly, the comparison of tweets made using hash tag and non-hash tag also revealed that equal percentages of tweets were made using hash tag on the weekday while 43% tweets were made using hash tag in weekend and 57 without hash tags. The following charts summarize the combined data pertaining to the use of the hash and non-hash tags on the weekday and the weekend (refer to appendix fig 17 and fig: 18). Conclusion From the above study, it can be comprehended that social mining gives a new destination to the entire human being. People across the world are seen to be currently engaged in making extensive use of social networking sites. Various reasons can be attributed towards its increasing popularity. In this regard, lower cost than other communication channels can be ascertained to be one of the prime reasons behind the increasing use of social networking sites. The objective of the study was to explore and compare the use of frequency of tweets made on the weekday and weekend across the selected Australian States. The analysis of data revealed interesting facts regarding the tweets made by the people in the different states of Australia on the weekday and weekend. Accordingly, it was observed that the tweets made in the different states of Australia significantly vary with one another. In the states like South Australia (SA) maximum tweets were made on the sports while in other states like Western Australia (WA) maximum tweets were made on place. Notwithstanding, profound variation was also identified with respect to the frequency of tweets made on the weekday and weekend. In this regard, it was observed that average 30% of tweets were made on place across all the states on the weekday while average of 54% tweets were determined to be made on sports on the weekend. At the same time, it was further observed that an equal percentage of tweets were made using hash tag and non-hash tags in all the states that were undertaken in the study on the weekday while on the weekend, 43% tweets were identified to be made using hash tags and 57 tweets with hash-tags. References Bryman, A. 2012. Social Research Methods. Oxford University Press. Kothari, C. R. 2004. Research Methodology: Methods and Techniques. New Age International. Krishnaswamy, K. N. & et. al. 2009. Management Research Methodology: Integration of Principles, Methods and Techniques. Pearson Education India. Russell, M. A. 2011. Mining The Social Web: Analyzing Data From Facebook, Twitter, Linkedin, and Other Social Media Sites. "OReilly Media, Inc.". Appendix Fig:1 Fig: 2 Fig: 3 Fig: 4 Fig: 5 Fig: 6 Fig:7 Fig: 8 Fig:9 Fig: 10 Fig:11 Fig: 12 Fig:13 Fig: 14 Fig:15 Fig: 16 Fig: 17 Fig: 18 Read More