Modern era social networking has been revolutionised with the advent of Web-based social networking systems. This kind of communication platform is technically termed as a social networking site (SNS). The social networking sites have collectively given rise to social media networks that facilitate real-time communication and information sharing across the globe. Major social networking websites in the world such as Facebook, Twitter, Google Pulse, etc. generate lots of time critical dynamic data. The Pew Research Center (2014, p. 1) has described social media in the following words:
“Social media include all the ways people connect to people through computation. Mobile devices, social networks, email, texting, micro-blogging and location
These network structures can be visualised, analysed, and processed to generate network insights. The network insights are used to obtain valuable information on different aspects of social media functions, structures, variations, and behaviours.
Data mining can help in manipulating social media data more effectively. According to Gundecha and Liu (2012, section 1.1), data mining can be defined as “a process of discovering useful or actionable knowledge in large-scale data.” Given the diversified nature and hugeness of social media data, implementation of data mining techniques has emerged as a coveted alternative to conventional social network analysis (SNA) methods. In this paper, the main focus area is the field of data mining with reference to social media analysis and research.
2. Literature
Instead, social media data and its features must be understood on the basis of data sources and network models related with them (Zafarani, Abbasi, and Liu 2014).
2.2 Application of data mining The state of the art of modern data mining technologies is highly complex, rich, and purposive. According to Han, Kamber, and Pei (2011), basic essentials of data mining include pre-processing, supervised and unsupervised learning, algorithmic manipulation, and effective organisation. Furthermore, clear understanding of graph essentials, network measures, and network models (see Figure – 2) is extremely necessary for implementing a “social media mining” (Zafarani, Abbasi, and Liu 2014, p. 1) framework
According to Gundecha and Liu (2012), the major aims of a data mining process include manipulating large-scale data and deciphering actionable patterns in them.
“Because social media is widely used for various purposes, vast amounts of user-generated data exist and can be made available for data mining.” (Gundecha and Liu 2012, section
This world as we know is heading towards a more virtual era, where everything we need to know is under the palm of our hands. We have many devices such as smart phones, tablets, computers, which gives us access to an infinite amount of information. This virtual life style we are becoming accustomed to introduced us to social media. An increase amount of interaction is being built between known and unknown users from all around the world. Social networks such as Facebook, MySpace, twitter, and even tumbler have become an everyday routine of our daily lives. In this modern society, all these social media websites have brought about a significant amount of impact in many of us. It has really influenced its users on how to conduct their lives.
One important aspect of the tweets is that they have highly structured data about different aspect of the actual communication like location, language, individuals, time, etc. Twitter keeps track of different pieces of relevant information in JSON format and we can model such information to our greater use. This associated information is useful for a variety of purposes, including but not ...
Big Data, Predictive Analytics and Data Mining have other important applications that do not embody direct impact over managerial strategy in a company; nonetheless, they represent a significant tool in society. These include the successful use of Big Data in astronomy (e.g., the Sloan Digital Sky Survey of telescopic information), politics (e.g., a political campaign focused on people most likely to support a candidate based on social networks or web searches) (Murdoch and Detsky, 2013), and education, where Data Mining offers educational institutions additional approaches to improve graduation rates of students, students' success and learning outcomes, through prediction, cluster analysis, association and classification by info-data informatics tools (Beikzadeh, Phon-Amnuaisuk, and Delavari, 2008).
Social networks are increasing dramatically every year. Employers are turning to social networks because it is a tool to screen job applicant’s profiles. According to a survey conducted by jobvite.com (2013), 94 % of employers use social media profiles to recruit job applicants. This trend assists the applicants and recruiters. Job applicants should be judged by their social network profiles because social media give positive image about the candidate, prove the information in the resume, and help to identify if the person fits the culture of the company or not.
Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems.
Social media analysis involves processing huge amounts of unstructured data to gain useful insights, in order to do this, various data collection and analysis tools and techniques have to be utilized. These techniques are based on three fundamental models, namely, statistical models, survey models and prediction market models.
Our every day life has changed forever, thanks for the ubiquitous smart phones and technology dependent information age. We leave a trail of data while travelling, shopping, driveing, bloggin, and even voting. All of these activities leave a digital signature unique to us, which if analyzed can predict our next move. Similarly, a large set of data is being created each day by businesses, researchers and the World Wide Web. According to an estimate by the government, there are about 1.2 zettabytes (250 billion DVDs) of electronic data generated each year by everything from underground physics experiments and telescopes to retail transactions and Twitter posts (Mervis 22). This data growth has created a new challenge and opportunity. The challenge is that, we don’t have sufficiently trained people to analyze Big Data. The opportunity is that if you have the right resources who can transform Big Data to a meaningful use, you guarantee the success of your business.
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
The Culture of Connectivity (Dijck, 2013) explored the changing face of social media as it evolved with the advent of Web 2.0. We now have large corporations accomplishing more than just facilitating connection, they have created global information and data mining companies that extract and exploit user connectivity. The development of this connectivity by preeminent social media platforms such as Facebook and Twitter has influenced, transformed and constrained the potential for connection via social media. There have been fundamental changes in social media and this connectivity is not without controversy.
Internet has become a vital element in people daily lifestyles. People use smart phones, tablets, laptops or computers to access Internet. By the first decade of the 21st century, many Internet users use faster broadband Internet access technologies. As the Internet users grow, one of the Internet phenomenons that can be seen is social networking. Basically, people use social media to interact among people where they create, share or exchange information in virtual communities and networks.
Social media has become a major epidemic in today’s society. According to millions of people have signed up on social media websites, allowing their basic information to be shared with the world wide web. Two of the biggest social media websites today are Facebook and Twitter. The new generation tends to use Twitter over Facebook, the older generation prefer Facebook over Twitter. Though Facebook and Twitter serve the same purpose and have many similarities, they both differ in many ways.
Data mining has four stages: collection, aggregation, interrogation and prediction (de Zwart et al 2014, p. 715-719). As with filtering, this process more often than not happens without the knowledge of users and regardless, it is “almost impossible” to prevent (p. 716). The importance of user consent has already been touched upon, as has the implications for the balance of power between social media platforms and the users. As social media implicitly exists for users to interact with one another , for the power to remain out of the hands of the users is especially problematic as it in many ways contradicts the supposed purpose of social media (Baym 2015, p. 1-2). More specific to data aggregation and interrogation, the power imbalance works against the freedom of expression and instead restricts the ability of the individual to control their own identity and the way it is presented. Data aggregation, or the collection of data over time, has no way or retaining context for specific actions or interactions (de Zwart et al 2014, p. 716). The previously discussed context collapse comes into play once more, as the way a individual may present themself in interactions with various other users may form what appears to be contradictory information. The interrogation of this data then becomes the
Social media is a highly interactive platform for individuals and communities to share, create, discuss and edit user-generated contents, with the help of online and mobile technologies (Kietzmann et al., 2011). Given the widespread exposure of social media in today’s context, it seems we are in the midst of a revolutionized ecosystem of communication. It is no doubt that this social media ecosystem can be considered one of the most effective ways of communication by far. The Honeycomb of Social Media below (Fig.1) shows the Social Media functionality
This research paper is going to be about how social media impacts an individual. Then it is going to be about how much of an impact it has on businesses. Afterwards, we are going to go over the pros and the cons of social media in society. Then this research paper is going to conclude on where social media is heading towards in the near future and so on. Social media can impact the life of an individual at many levels.
Nowadays, social media is growing very rapidly throughout the whole world. Social media has changed the way that we communicate with others through using these common social networking sites like Face book, Twitter, and Instagram…For that, social media has positively and negatively impacted our life.