Companies Are Using Your Social Media Data Research Paper

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The decision social networks face on how to monetize their content and fuel new growth is predicated on data mining and business intelligence techniques, ethicacy of how customer data is used, and their strategies for long-term growth. How social networks use subscriber data is the area of analysis this paper addresses. In evaluating how social media data is being used by social networks today, the concepts of advanced analytics including latent semantic indexing, data mining, and the eventual development of data services is explored. The use of social network customer data for improvement of applications online is however secondary to the creation of advertising-driven business models. In addition, the use of social media data for the development of entirely new approaches to Customer Relationship Management (CRM) application is also discussed. Of the many potential direction that social network providers could go strategically, the most probable is the development of Social CRM (SCRM)-based applications and services, entirely delivered online. SCRM will be the catalyst for redefining relationships from both the B2B and B2C marketing context, attaining the goal of having a true 360-degree view of the customer for the first time. The greater valuation of social media data is not in the advertising models but in the customer-driven business processes companies globally are challenged with daily in growing their businesses. The ethical use of social media data will be debated for decades to come as the founders and leaders of these companies see any information shared as their asset or property, arguing it is in the public domain. Ownership of data will redefine business models.

Literature Review


TOPIC: Research Paper on Companies Are Using Your Social Media Data Assignment

The principles of reciprocity, responsiveness, transparency and trust, the greatest virtues of humankind, are what will lead people to connect with each other online and communicate. The phrase "joining the conversation" has become ubiquitous in the area of social networks (Bernoff, Li, 2008). Many entrepreneurs launching social networks were previously community managers who received formal educations on techniques for creating high levels of collaboration and communication in diverse groups and their start-ups reflect their values in this regard (Fischer, Reuber, 2011). Social networks were initially seen as a powerful catalyst that would elevate the key influencers in every industry from being thought leaders to rock stars of their fields, with exponentially greater impact on purchasing decisions (Mui, Mohtashemi, Halberstadt, 2002). Today the value of what friends and associates who are trusted outweigh what influencers say or do online however. This is leading to a revolution in how people use, trust and create businesses from social media data.

The Most Elusive Asset of All Is Trust on Social Networks

The ecosystems that are emerging out of social networks however have an arbitrary and often continually evolving concept of just what trust is. What often happens as a result is that trust from one ecosystem to the next can be betrayed as a result, or as Mark Zuckerberg has done on occasion, redefine privacy settings across hundreds of millions of users unilaterally (Collins, 2010). As trust is the new currency in all social transactions, the role of ethics is critically important for navigating the strategy decisions social networks need to make (Mui, Mohtashemi, Halberstadt, 2002). The use of social network data needs to be managed by a systematic methodology that applies insights gained from unstructured analysis, presents it back to the user, and shows just how their personal opt-in data and activity say about them from a digital footprint standpoint (O'Hara, Alani, Kalfoglou, Shadbolt, 2004).

Utilitarian Ethics and Social Media Data Use

Defining decision frameworks that seek to promote the shared greater good over the individual gain, while mitigating losses across all members of a community is what John Stuart Mill had in mind when he defined utilitarianist ethics (Mill, 1861). There are many parallels to utilitiatiran ethical theory and the trajectory of trust on social networks (Jonsson, 2011). The development of ad hoc trust-based networks and the defining of utilitarian-based workflows are in theory what the founders of social networks considered to be their ethical and moral compass (Fischer, Reuber, 2011). Left to its own trajectory of growth, social networks would over time arbitrate trust through balkanization and "walled gardens" or virtual gate guarded communities. Semantic analysis would be used to validate if someone was telling the truth or not, and the ethics of showing whether trust was warranted (Mui, Mohtashemi, Halberstadt, 2002).

Defining a Technology of Trust Based on Utilitarianism

The areas of latent semantic indexing, linguistic analysis and semantic analysis are all quickly converging on the areas of federated trust networks and sentiment analysis of social networks (Mui, Mohtashemi, Halberstadt, 2002). This rapid pace of innovation is fueled outside of social networks for analyzing and building linguistic models of the massive amount of unstructured content that government security agencies monitor to evaluate the risk of terrorist attacks, potential leaks of classified information, and assess risks to national security (Rishel, Perkins, Yenduri, Zand, 2007). These same technologies are being used specifically for defining semantic grids, using latent analysis, to determine the veracity and trustworthiness of content across all social networks (Olmedilla, Rana, Matthews, Nejdl, 2005). For the commercialization of data on social networks to be successful without causing segregation online, utilitarianism needs to be combined with linguistic modeling to ensure authenticity and trust.

Methods Section

Areas of Analysis

The majority of social media data is unstructured and defies easy categorization into taxonomies or any form of organization. Despite these challenges which are being addressed with initial efforts in the areas of Latent Semantic Analysis (LSI) (Wei, Yang, Lin, 2008) and sentiment analysis (Fan, Chang, 2010) there is a major gap between analytical tools and techniques to monetize social media content and using it to personalize the user experience. For Facebook the dilemma of increasing their privacy settings and creating a more stable, secure, and protected platform vs. The need to capture more data for its advertising-driven business model and unique product development strategies (Collins, 2010).

Methods of Analysis and Inquiry

Using Latent Semantic Index technologies and techniques (LSI) (Wei, Yang, Lin, 2008) to determine linguistic models in unstructured social media data will provide a benchmark for how effectively social networking sites are using the data. The use of LSI for sentiment analysis of contextual update data, or mostly tweets, will provide a baseline of either intention to purchase additional services, from the social network itself or its advertisers (Thelwall, Buckley, Paltoglou, 2011).



The uses of social media data vary from the continual improvement of the social networks' functionality and application depth in their existing applications, the use of the data to create entirely new applications including Social Customer Relationship Management (CRM). This third alternative is the one where Facebook seeks to redefine the ethicacy of social network-based data (Zimmer, 2010). The use of user's data to create advertising programs within Facebook is ethically the same as selling the data itself, saying the data was already public anyway (Zimmer, 2010). Google is considering how to gain access to social CRM data in the form of entirely new approaches to indexing all social networking sites, with the eventual goal of creating their own CRM system (Michel, 2009).

How Social Network Data is driving the Development Social CRM

The fundamental design objective of CRM is to provide a 360-degree view of the customer (Greenberg, 2008). Social networking sites hope to charge on a per-record basis to automatically sell millions of records to anyone with a social CRM platform interested in buying them. is an example of one company already with Application programmer Interfaces (APIs) and Google has created a comparable set to import massive amounts of social network data into the CRM applications they are widely rumored to have under development (Michel, 2009). Social networking users' records will be integrated into the eight building block areas of CRM as defined by Gartner in Figure 1: The Eight Building Blocks of CRM.

Figure 1: The Eight Building Blocks of CRM

(Source:, 2002)

Legal and Ethical Implications

The ethicacy of selling customer data records and data attributes to any business globally willing to pay the service charges and lease a social CRM instance on a SaaS platform have yet to be defined in the legal system. The opt-in aspects of choosing to share data with Facebook does not however grant them access rights to resell profile data to any business or entity interested in buying it. The future however shows that social networking sites' greatest potential revenue stream is that of a massive customer information data warehouse or data mart. The ethical implications of transforming what was once a trusted platform into a highly lucrative market data provider is being anticipated by Facebook already, with their position being that once the privacy settings on an account are defined and data entered, all data is considered resalable (Zimmer, 2010).

Analysis Section


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