Distortion of Social Media Data: Should Expressions be Controlled or Spammers be Eliminated?

How should we use social media? What should be language parameters and limits of expression?

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Technically speaking, there needs to be no limit at all from the perspective of a researcher. Limits of expression are not required. One can, indeed, use abusive language or create other kinds of mess. These things can be converted into research topics. However, problems arise because most of the social networking sites are doing something very wrong. They let users leave several user data fields vacant. This helps spammers to become users and distort the social media data considerably. In other words, netiquette is less important than network security. It appears to be so at least from the viewpoint of a researcher or technology enthusiast.

If someone needs to join Facebook, he/she may skip a number of questions. But Google has shown a good approach. Google often makes filling of certain fields in a form mandatory. This is likely to increase stability and reliability. While opening a Gmail account (that can be later connected with Google Plus, the company’s social networking service), the user must give an authentic phone number. If this kind of system is materialized for Facebook too, then spamming and unauthorized use can be considerably controlled. However, the primary responsibility still lies with email service providers rather than the social networking sites.

Extended Reading:

[1] Core Rules of Netiquette

[2] Laughton, P. A. (2008). Hierarchical analysis of acceptable use policies. South African Journal of Information Management, 10(4), 2-6.

[3] Social Spam

[4] Social Networking Spam – 5 Rules for Marketers

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Social Media Mining May Eat Up Data Mining Technology as a Whole

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This is really unacceptable, but it is indeed a compromising situation. Nevertheless, without a compromise of this kind, social media research cannot be enriched and popularized. The compromise involves a restrictive integration between social media analysis and data mining techniques. The basic philosophy underlying this socio-technological amalgamation can be stated as ‘What we get after we get something or everything?’ Breaking apart terminological barriers, this something-everything problem can be regarded as a new variety of requirements engineering issue. At one side, experts are having loads of data generated by social media platforms. On the other side, experts are wondering how data mining can be used optimally to understand and analyse social media trends. While doing the both tasks simultaneously, experts are now trying to restrict the scope of large-scale data mining applications inside the realm of social media. This conception, known approximately as social media mining, may help in drawing an eventual but erroneous conclusion. This potential conclusion is that data mining is best suited for organizing, managing, analyzing, and exploiting social media data only! Can the scientific world afford that the computer programmers of future almost solely dedicate themselves to social media mining research and development? In order to understand the risks of such an approach, please have a look to the following academic works and an informative weblog:
[1] Kaplan, Andreas M.; Haenlein, Michael (2010). “Users of the world, unite! The challenges and opportunities of social media”. Business Horizons 53 (1)
[2] Barbier, Geoffrey; Feng, Zhuo; Gundecha, Pritam; Liu, Huan (2013). “Provenance Data in Social Media”. Synthesis Lectures on Data Mining and Knowledge Discovery.
[3] Why Data Mining Is the Next Frontier for Social Media Marketing

Social Network Analysis and Social Media Mining

What are social networks? Social networks are neither directly related with social network analysis (SNA) nor they have any unswerving relationship with social media mining. Social networks have evolved many a times all around us down the antiquities and ages. A nice example of a social network is your locality, where you may have a strong liking for your neighbor at north and a weak attachment with the restaurant owner at south. Another social network is where you get into like an asteroid when you join a new office and a new workforce. Therefore, strictly speaking, analysis of a social network can be done by analog as well as digital means. Is that SNA … Did I say so? Of course, no.

Social networks can be better identified as digital networks developing among different users via computing and Internet platforms. Websites like Twitter, Facebook, etc. are there for serving this purpose of forming digital communities in the virtual space. SNA is concerned with analyzing the trends out there. A general definition is given by Freeman, where he states that “Social network analysis is focused on uncovering the patterning of people’s interaction. Network analysis is based on the intuitive notion that these patterns are important features of the lives of the individuals who display them” (Social Network Analysis). On the other hand, social media mining appears to be a more technical conception. What are social media? Your Internet-connected gazettes, computer systems, phones, etc. can all be used as social media. Multimedia implementations have been incorporated in social media like Facebook, YouTube, etc. So when we take an analytical turn across this topic of enquiry, we find that “Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining” (Social Media Mining: An Introduction).

In this way, one can understand that SNA has technical limits even from a conceptual point of view. But social media mining is technology oriented. On the other hand, social media mining may appear to be too technical, too pervasive, and boringly iterative. But SNA provides with less complex methods for understanding the social media landscape itself.

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