Social media mining

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Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.

Contents

Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. [1] In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others.

Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users with advertising. [2] Scientists and machine learning researchers extract insights and design product features. [3]

Users may not understand how platforms use their data. [4] Users tend to click through Terms of Use agreements without reading them, leading to ethical questions about whether platforms adequately protect users' privacy.

During the 2016 United States presidential election, Facebook allowed Cambridge Analytica, a political consulting firm linked to the Trump campaign, to analyze the data of an estimated 87 million Facebook users to profile voters, creating controversy when this was revealed. [5]

Background

As defined by Kaplan and Haenlein, [6] social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Instagram, Photobucket, or Picasa), news aggregation (Google Reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger).

The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegrees.com, was introduced in 1997. [7] Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.

Uses

Social media mining is used across several industries including business development, social science research, health services, and educational purposes. [8] [9] Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. [10] These forces are then measured via statistical analysis of the nodes and connections between these nodes. [8] Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. [11] This provides important social information about users' emotions on specific topics. [12] [13] [14]

These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. [8] Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. [8] Analysts also value measures of homophily: the tendency of two similar individuals to become friends. [10] Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. [11] These analyses can also help create recommendations for individuals in a tailored capacity. [8] By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with.

Perception

Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google. Companies such as these, considered "Big Tech" are companies that build algorithms that take advantage of user input to understand their preferences, and keep them on the platform as much as possible. These inputs, that can be as simple as time spent on a given screen, provide the data being mined, and lead to companies profiting heavily from using that data to capitalize on extremely accurate predictions about user behavior. The growth of platforms accelerated rapidly once these strategies were put in place; Most of the largest platforms now average over 1 billion active users per month as of 2021. [15]

It has been claimed by a multitude of anti-algorithm personalities, like Tristan Harris or Chamath Palihapitiya, that certain companies (specifically Facebook) valued growth above all else, and ignored potential negative impacts from these growth engineering tactics. [16]

At the same time, users have now created their own data arbitrages with the help of their own data, through content monetization and becoming influencers. Users typically have access to a varied set of analytics specific to people that interact with them on social media, and can use these as building blocks for their own targeting and growth strategies through ads and posts that cater to their audiences. Influencers also commonly promote products and services for established brands, creating one of the largest digital industries: Influencer marketing. Instagram, Facebook, Twitter, YouTube, Google, and others have long given access to platform analytics, and allowed third parties to access that information as well, at times unbeknownst to even the user whose data is being viewed/bought. [17]

Research

Research areas

Publication venues

Social media mining research articles are published in computer science, social science, and data mining conferences and journals:

Conferences

Conference papers can be found in proceedings of Knowledge Discovery and Data Mining (KDD), World Wide Web (WWW), Association for Computational Linguistics (ACL), Conference on Information and Knowledge Management (CIKM), International Conference on Data Mining (ICDM), Internet Measuring Conference (IMC).

  • KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • WWW Conference – International World Wide Web Conference
  • WSDM Conference – ACM Conference on Web Search and Data Mining
  • CIKM Conference – ACM Conference on Information and Knowledge Management
  • ICDM Conference – IEEE International Conference on Data Mining
  • Association for Computational Linguistics (ACL)
  • ASONAM conference - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • Internet Measuring Conference (IMC)
  • International Conference on Web and Social Media (ICWSM)
  • International Conference on Social Media & Society
  • International Conference on Web Engineering (ICWE)
  • The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML/PKDD),
  • International Joint Conferences on Artificial Intelligence (IJCAI),
  • Association for the Advancement of Artificial Intelligence (AAAI),
  • Recommender Systems (RecSys)
  • Computer-Human Interaction (CHI)
  • Social Computing Behavioral-Cultural Modeling and Prediction (SBP).
  • HT Conference – ACM Conference on Hypertext
  • SDM Conference – SIAM International Conference on Data Mining (SIAM)
  • PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining

Journals

  • DMKD Conference – Research Issues on Data Mining and Knowledge Discovery
  • ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
  • IEEE Transactions on Knowledge and Data Engineering (TKDE),
  • ACM Transactions on Knowledge Discovery from Data (TKDD)
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Social Network Analysis and Mining (SNAM)
  • Knowledge and Information Systems (KAIS)
  • ACM Transactions on the Web (TWEB)
  • World Wide Web Journal
  • Social Networks
  • Internet Mathematics
  • IEEE Intelligent Systems
  • SIGKDD Exploration.

Social media mining is also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases.

See also

Methods
Application domains
Companies
Related topics

Related Research Articles

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  58. ↑ Gao, Huiji; Tang, Jiliang; Hu, Xia; Liu, Huan (2014). "Content-Aware Point of Interest Recommendation on Location-Based Social Networks" (PDF). Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Archived from the original (PDF) on September 24, 2015. Retrieved December 1, 2014.
  59. ↑ Gao, Huiji; Tang, Jiliang; Liu, Huan (2014). "Personalized Location Recommendation on Location-based Social Networks" (PDF). Proceedings of the 8th ACM Recommender Systems Conference. Archived from the original (PDF) on September 24, 2015. Retrieved December 1, 2014.
  60. ↑ Barbier, Geoffrey; Feng, Zhuo; Gundecha, Pritam; Liu, Huan (2013). "Provenance Data in Social Media". Synthesis Lectures on Data Mining and Knowledge Discovery. 4: 1–84. doi:10.2200/S00496ED1V01Y201304DMK007. S2CID   46794494.
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  62. ↑ Gundecha, Pritam; Barbier, Geoffrey; Tang, Jiliang; Liu, Huan (2014). "User Vulnerability and its Reduction on a Social Networking Site" (PDF). ACM Transactions on Knowledge Discovery from Data. 9 (2): 1–25. doi:10.1145/2630421. S2CID   1200227. Archived from the original (PDF) on March 3, 2016. Retrieved December 1, 2014.
  63. ↑ Marozzo, Fabrizio; Bessi, Alessandro (2018), "Analyzing polarization of social media users and news sites during political campaigns", Social Network Analysis and Mining, 8: 1, doi:10.1007/s13278-017-0479-5, S2CID   21257844
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