Social Media Mining

Social Media Mining

Roberto Marmo (Universita' Pavia, Italy)
DOI: 10.4018/978-1-7998-3473-1.ch149
OnDemand PDF Download:
No Current Special Offers


Individuals produce a large amount of freely available data by interacting, sharing, and consuming content through social media. Social Media Mining is a systematic analysis of information generated from social media. Therefore, it is possible to known media usage, online behaviors, sharing of content, connections between individuals, online buying behavior, etc. These patterns and trends are of interest to organizations, brand, businesses, marketers, sociologists etc. This chapter aims to introduces to the concepts of social media mining, processes and tools involved in mining and processing data from social media platforms, as well as the importance, privacy, challenges, and use cases.
Chapter Preview

Social Media Mining

Social Media Mining (SMM) is the process of representing, analyzing, and extracting actionable patterns and trends from massive raw social data, that are publicly available on social media web platforms (Bonzanini, 2016; Ravindran, 2015; Zafarani, 2014).

SMM discusses theories and methodologies from different disciplines such as computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics.

The term mining is an analogy to the resource extraction process of mining for rare minerals, as stated in typical data mining approaches that analyze the huge volumes of data and seeks out patterns, trends and clusters. The goal of work (Injadat, 2016) is to analyze the data mining techniques that were utilized by social media between 2003 and 2015, conclusions suggest that more research be conducted by both the academia and the industry since the studies done so far are not sufficiently exhaustive of data mining techniques.

Data generated on social media sites are different from conventional attribute-value data for classic data mining. Social media data are largely user-generated content on social media sites. Social media data are vast, noisy, distributed, unstructured, and dynamic. These characteristics pose challenges to data mining tasks to invent new efficient techniques and algorithms.

SMM requires human data analysts and automated software programs to sift through massive amounts of raw social media data in order to discern patterns and trends. SMM cultivates a new kind of data scientist who is well versed in social and computational theories, specialized to analyze recalcitrant social media data, and skilled to help bridge the gap from what we know (social and computational theories) to what we want to know about the vast social media world with computational tools (Zafarani, 2014). The key questions for researchers are:

  • How to derive information from unstructured data as textual, image, video?

  • How to form analysis to assist in decision making based on the information derived from unstructured data?

  • How to grasp opportunities for business success based on the generated report?

  • Which source of social information should a user use?

  • How can to identify communities in a social network?

  • Which pieces of information are popular and receive a lot of attention?

  • How quickly to uncover insights about customers? Who is customer talking to? Who are his friends? Who is influencing him?

Key Terms in this Chapter

API: Set of procedure definitions and protocols that describe the behavior of a software component, such as a library or remote service, in terms of its allowed operations, inputs, and outputs.

Social Media Marketing: The process of gaining website traffic or attention through social media sites. Social media marketing programs usually center on efforts to create content that attracts attention and encourages readers to share it with their social networks.

Social Analytics: The extraction of valuable hidden insights from vast amounts of social media data to enable informed and insightful decision making.

Ethics: What is deemed to be good or bad in human conduct.

Social Network Analysis: Mathematical technique developed to understand structure and behaviour between members of social system, to map relationships between individuals in social network.

Social Media Mining: The process of representing, analysing, and extracting actionable patterns and trends from raw social media data.

Complete Chapter List

Search this Book: