Big Data In Social Media ✓ Solved

Big Data In Social Media

Big Data in Social Media By definition, Big Data can simply be termed as voluminous data. In more specific definitions, it can be termed as that which is large, complex and fast and a s a result, is not in a position to be processed using the typical traditional methods of data processing. The volume, variety, velocity, variability and veracity are used in the categorizing of data as big data. With the development in technology, and the continued incorporation of these technological sources into our day to day lives, the collected data through the Internet of Things among other information systems has resulted in big data (Ivanov, 2018). One such areas where Big data is found is in social media platforms.

As opposed to the olden days, currently, more and more people and companies are using social media daily to achieve their specific objectives and goals, it is estimated that social media platforms like Facebook produce data as big as 500+ terabytes in a s ingle day! Most of these data in the social media are as a result of the videos, photos, messages and comments being shared across the media platforms. Not only do individuals use social media to keep in touch, but companies also use it in a concept called social media marketing. Through the media, and using big data analytics, companies are able to map out consumer behavior through what they like and what they share (Nicora, 2019). They use these platforms to reach their target audiences and at the same time use them to get feedback from their clients.

As a result, the amount of data from social media platforms is not only voluminous, it is also heterogenous in the sense that it contains both nominal and numeral values from different places, it is variable in that it has unpredictable flow, it is fast because it is collected in real time. This qualifies the data to be Big Data and requires big data analytics to process. References Ivanov I. (2018) What is Big Data Analytics on Social Media? Iocowise. Retrieved from Nicora R. (2019) How is big data impacting social media? Medium. Retrieved from

Sample Paper For Above instruction

Introduction

Big Data has revolutionized various sectors, especially social media platforms. Defined as large, complex, and rapidly generated datasets, Big Data's distinctive features—volume, variety, velocity, variability, and veracity—make traditional data processing techniques inadequate.

The Concept and Dimensions of Big Data

Big Data refers to datasets that surpass the processing capacity of conventional tools. The five Vs—volume, variety, velocity, variability, and veracity—are essential in categorizing data as Big Data. Technological advancements and the proliferation of Internet of Things (IoT) devices have significantly contributed to the explosion of social media data. Platforms like Facebook generate terabytes of data daily, comprising videos, photos, messages, comments, and other user-generated content (Ivanov, 2018). The development of social media has not only transformed personal communication but also marketing strategies for businesses, integrating Big Data analytics to understand consumer behaviors.

Social Media Data Characteristics

Data collected from social media is characterized by its heterogeneity, containing both nominal and numerical data from diverse sources. Its unpredictable flow and real-time collection make it inherently variable and fast, requiring sophisticated analytics tools for meaningful interpretation. This data's vastness and dynamic nature exemplify the core attributes of Big Data, which demand specialized analytics and processing techniques to extract actionable insights.

Implications for Business and Research

The analytical potential of social media Big Data has profound implications for marketing, customer engagement, and strategic decision-making. Companies leverage Big Data to understand preferences, track brand sentiment, and customize marketing efforts, thus maintaining competitive advantage (Nicora, 2019). Academically, the rich datasets provide opportunities to study societal trends, behaviors, and patterns, albeit with challenges related to data privacy, security, and ethical considerations.

Challenges in Processing Social Media Big Data

Handling social media Big Data presents several challenges. The heterogeneity and volume necessitate powerful storage solutions and processing capabilities. Ensuring data quality, reducing noise, and addressing privacy concerns are critical. Additionally, extracting meaningful insights demands advanced algorithms, machine learning models, and real-time analytics, which require significant computational resources and expertise.

Technological Tools and Techniques

To manage social media Big Data effectively, various tools such as Hadoop, Spark, and NoSQL databases are employed. These facilitate scalable storage and fast processing. Techniques like sentiment analysis, network analysis, and natural language processing enable deep insights into user behaviors, trends, and community structures. As demonstrated in research such as Moessner et al. (2018), these methodologies can be applied to study sensitive issues like eating disorders, illustrating the dual-edged nature of social media data.

Conclusion

The integration of Big Data analytics in social media has transformed research and business practices. While it offers unprecedented insights and opportunities, it also poses significant technical, ethical, and privacy challenges. Progress in technology and methodological innovations will continue to shape the effective utilization of social media Big Data in the future.

References

  • Ivanov, I. (2018). What is Big Data Analytics on Social Media? IoCowise.
  • Nicora, R. (2019). How is Big Data Impacting Social Media? Medium.
  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social Big Data: Recent Achievements and New Challenges. Information Fusion, 28, 45-59.
  • Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 128-146.
  • Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 1-11.
  • He, W., Wang, F. K., & Akula, V. (2017). Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management.
  • Moessner, M., Feldhege, J., Wolf, M., & Bauer, S. (2018). Analyzing big data in social media: Text and network analyses of an eating disorder forum. International Journal of Eating Disorders, 51(7), 742-753.
  • Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2019). Role of Big Data and Social Media Analytics for Business to Business Sustainability. Industrial Marketing Management.
  • Additional references as relevant for comprehensive coverage.

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