Social Media


Social media scene has created a lot of interest from people of various backgrounds, and recently social scientists who want to understand human behavioral patterns. The study concerned itself with the effectiveness of social media analytical tools in the interpretation of big data as well as the efficiency of the tools in the use of predictive models. The study was exploratory in nature and used qualitative data in sourcing for the information, therefore applying a qualitative research design approach to derive findings. The results indicated that social media analytical tools are categorized according to the function they serve and that predictive model have benefitted from the vast array of tools available in the market such that they can accurately predict future trends. The study reveals that there is a wide availability of social media analytical tools and each has its characteristic making it difficult to measure the reliability and validity of the information provided accurately. Therefore, researchers need to create more comprehensive research designs to combat the dynamic nature of social media analytics.

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The explosion of social media has created a vast opportunity for social scientists, and coupled with it is the emergence of big data analytics which concerns itself with making sense of the data derived from the social media sites. The data is being harnessed to study human behavioral change using the myriad of tools, and it is creating an evolution in the social sciences. There is a huge challenge, however, because the data from social media is messy with a lot of unstructured data and a lot of contradictory information and it begs development of new methodologies in research. Social science is a field that concerns itself with collecting unbiased information from representative samples and analysis of that data that will reflect social reality while operating under a strong ethical framework in protecting its subjects. The study used a qualitative research methodology because of its flexibility collection of the huge amount of data as demanded by social science.

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The study employed the qualitative research approach design and focused on conducting document analysis to gather information. The materials used for the study were primarily focused on answering the research questions asked with a particular bias towards those that were able to address the issue of usage of big data in social media. The study was exploratory in nature, that is, it was aimed at understanding the phenomenon around the explosion of the use of big data in analyzing social media. To derive a suitable understanding of the phenomenon discussed, massive amounts of hard data were analyzed, which was sourced from publications, print media, and electronic sources. Analysis of the data that was gathered was vital in gaining an understanding of the emerging trend of whether businesses are finding any effectiveness in using the numerous data sets mined from social media platforms.

To analyze the data acquired, it was categorized into sections that provided information on the various categories of analytical tools available in the market, and lastly on how effective those tools were in interpreting data from social media. The data used to address the research questions was widely available because of the dynamic nature that has been as a result of the explosion of social media. Interestingly, the rapid growth of the social media sites coincided with the emergence of a family of tools that are used for big data analytics, and the combination of this factors enabled the answering of the research questions at hand. However, the immense challenge that came with the full availability of the data was whether it was truthful and reliable. Many of the datasets that are used by the social scientists are derived from third party web pages, or by querying public APIs.

Although the observation of social media behavior from internet sources can provide suitable information, there is a difficulty in producing proper samples from the online populations meaning that various measurement errors are plaguing many of the data collection methods. Having an understanding of those facts, the study used data that had was mainly presented after thorough research from peer-reviewed journals, academic papers from credible institutions, and information from technological companies that have conducted research in the field over the years. The data gathered from these sources gave certainty that the information had been passed through both validity and reliability tests.


As already elaborated through the research questions, the study focused on finding various analytical tools and describing their effectiveness in interpreting data derived from the various social media sites. This section describes what kind of methods were used in answering the research questions and the various findings that the studies conducted were able to reveal.

1. What analytical tools are used in the interpretation of big data in social media?

To answer the question, secondary data was used, and a descriptive research design approach was used. The data was gathered from the enormous research studies that have been conducted by social scientists in trying to contend with the big data phenomenon. From the numerous tools that have elaborately discussed in the studies, those that seemingly had the most impact for many of the businesses were narrowed down upon. The data presented revealed that numerous tools are employed to provide suitable analysis of the data that is very dynamic from the social media sites. The significant finding that the data from the secondary sources revealed is that the various analytical tools are often divided into different categories because of the different functions that each tool is created to perform. The main categories that the tools fall under were scientific programming tools, business toolkits, social media monitoring tools, and lastly text analytics tools. From those categories, a particular tool was chosen after a thorough search into its preferability in the business community as well as its ratings on effectiveness from various sources. The tools were products of research and were hard coded by various organizations, and each of the tools had a particular niche in which it operates because many factors are considered in analyses of the data derived from the social media sites. Each tool has its shortcomings because of what is referred to as noise from the enormous data gotten from social media.

2. How effective are the analytical tools used in the interpretation of big data in terms of Predictive Models?

To answer the question, information on various models that are used in the interpretation of big data had to be gathered. Just as in the case of the various analytical tools, secondary data was used, and a descriptive research design approach applied. The study paid close attention to the predictive models used widely in the interpretation of big data. The information gathered revealed that the model was particularly interesting to many of the social scientists because of its ability to be used in the predictive analysis. The majority of the businesses are not only interested in the real-time data that is generated through the social media but also want to have the ability to predict the future trends revealed by patterns in the enormous data so as to inform the strategies that they need to employ to secure their stay in business. The finding from the sources used in the study of predictive models revealed that predictive analysis was essentially a daunting task and the greatest challenge was in the volume of data that was supposed to be fed into the model so as to achieve credible future predictions. The studies revealed that because of the availability of the various tools available in the market, it is now easier to store, analyze, monitor and visualize data. A combination of these abilities was suitable in ensuring that predictive models were able to produce more accurate results when it comes to predictive analysis.

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Discussion of the Findings

Because of the vast amounts of data that are generated on a daily basis from the social media sites, there is a need for tools that can be able to search and source the information as fast as the data is produced. The findings of the study enumerate the tools that are used to achieve effective analysis of the huge data sets. The study shows that the scientific tools have been developed, and one of the primary tools that have been perfected to achieve that is by Math Work Inc. called MATLAB. The study also revealed that the main data gathered from social media is the sentiment of the masses, and to cover the need of measuring peoples feelings and their attitudes primarily predisposed on their choice for certain products in the market, and such a tool was valuable. One of the gigantic companies that concern itself with data in our age, that is Google, created a product they called Google Trends where such sentiments could be captured and interpreted in real time.

The business community which was the foundation of the study have tools that are tailored to suit their needs, and which are used for commercial purposes and the study revealed that one powerful tool available was SAS Social Media Analytics. Finally, because social media users often use slang language to pass their sentiments, there was a need for a tool that is bale to translate the natural language into an understandable one for effective interpretation. Such tools exist, but one that was being rapidly gaining traction to achieve that was the Social View tool that was developed under a partnership of two software companies, that is, Amplify and Visual Intelligence. Every business as revealed under the study has their reason as to the choice of the tool they wish to employ to achieve their business needs, and there is no shortage of such tools found under the different categories. The study also points out to the fact that predictive analysis can now be effectively achieved through the use of the tools. Finding data to be fed into the predictive models has been made easier because of the availability of the tools. Analysis of future trends through reading of the sentiments expressed through social media is now easier, and the model serves accurately in proving such analysis.

The results of the data gathered from the study indicate that social media is a complex science that is surrounded by vast issues that range from moral hazards not addressed by conventional ethical frameworks because of the intrusion of private data and use of the data to indicate different sentiments. There is also a rush in trying to compete with various companies to create different analytical tools that prove just to be a commercial venture. The problem with the huge competition is that most tools just roll out information that cannot be easily understood by users. However, while there are numerous challenges, many businesses are bale to have a competitive edge and market their products and services effectively because of the use of the analytical tools. The economy is thus able to benefit because of the numerous opportunities brought about reading consumer preferences through their sentiments.


The study will be able to open up the value of social media to the various actors concerned with the big data phenomenon. There is a huge interest in the number of people that want to understand individuals and society, and social media has provided such an avenue. Peoples sentiments can be turned into practical information to be used for business purposes as revealed through the study as analytical tools are now widely available under the different categories discussed. The findings in this study can inform both producers and consumers of research on the practices employed in social media analysis, how useful the tools are in improving business trends, as well some insight on the ethical side of social media science by revealing how companies involved in the analytics business use the data they mine.

However, the study only focuses on specific tools and one model to inform on the effectiveness of the social media analysis tools in the interpretation of big data. The challenge with this is that it may occur to provide a bias in the selection of the tools because other tools are widely available and probably serve better in function. Another challenge is that social media and big data are very dynamic topics and information that is seemingly relevant today may be obsolete in the future, and this is a barrier to effective research. Although this is the case, researchers need to build credible research designs to address issues with reliability and validity of data because the web is full of information, but not all of it is credible.

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