For several hundreds of years, official agencies have been studying techniques and mechanisms to identify individuals. They started off by passports and identity cards and later developed to more controversial schemes like DNA profiling and body surveillance (Caplan & Torpey, 2001).

It is estimated that there are 39 million web servers worldwide that host 3 billion indexable web pages with 20 billion links. There is an ever increasing surveillance by government as well as telecom operators at the cost of privacy of netizens (Batty, 2003). Technological advances in identity and behaviour mapping have become more daring in the recent times. The handheld mobile phones and other gadgets have made it possible for businesses get to know about the behaviour of the people and allow them to gather vital information that can help them reach out to these users. Phone manufacturers, software developers and internet search engines are now able to detect the behaviour and interests of the users through integrated algorithms and computing devices.

Cyber Body Language is best understood as “Context-Awareness” where a device or software is designed, primarily or partly, to analyse the behaviour or pattern of the users and apply information gathered to automatically assert products, services, or other purposes such as security monitoring.

This article covers the implications of Cyber Body Language’s Context-Awareness and how it will affect the users in terms of privacy, finances and consumption. The review of related literature discusses Cyber Body Language, Context-Awareness, Context-Awareness Computing, Privacy, Geolocations and Targeted Ads through personalized hypermedia application.


According to Oracle (2014), Cyber Body Language or “Digital Body Language” is similar to facial expression or behaviour a user makes when interacting in the cyber world. In an online equivalent, these behaviours and expressions could be web browsing history, download history, web searches and online communication. This behaviour is the raw data that provides informaton about the user’s interests, needs and so on. Even the schedule of the user’s online presence can be useful information for the organizations monitoring the user’s behaviour (Oracle, 2014).

The transformative shift of physical activities such as online shopping transactions had created a marketing challenge of comprehending online consumer behaviour (Woods, 2009). Oracle (2014) stated that marketing and sales operations need to be adapted to ensure that it is Context-Aware or able to comprehend the Cyber Body Language of the consumers. It is imperative that the organization must first have a broad understanding of the impact of the shift and how all the processes came to change with it. An organization must be well-equipped with the necessary technology and infrastructure to be able to synthesize the information based on the consumer behaviour. (Oracle, 2014)


Dey (2001) defined context as any data that can be utilized to describe the environment of an entity. According to him, an entity can be the user, location or a thing that is significant in the domain of the application or software (Dey, 2001). On the other hand, Context-Awareness is defined as someone who is the user of the information. In such as case, a system is said to be Context-Aware when it has the ability to gather and synthesize the context information and apply it in the improvement and adaptability of the device (Byun & Cheverst, 2004).

Context-Awareness is aimed to provide efficiency and usability of service offered to the users and this is only possible through being flexible and aware of the changing behaviors of the users (Bolchini, Schreiber, & Tanca, 2007; Dey, 2001; Zhu, Mutka, & Ni, 2005). It has been said that context played a very crucial role because it is built up from user information and included data on status, location and interests (Korpipää, Mäntyjärvi, Kela, Keränen, & Malm, 2003; Kwon, 2004).


In understanding Cyber Body Language, there were Context-Aware Systems developed that take advantage of user behaviour. Context-Aware Systems gather context, analyse such context gathered and then with the information acquired is used to customize the system based on the behaviour or changing situation of the user (Khattak et al., 2014).

Facebook plans to figure out the emotional state of the users. It files a stir of patents that try to find out our emotions. One of the patents is Augmenting Text Messages with Emotion Information which involves decorating the text messages to fit the people’s moods. Therefore, Facebook intends to join some features with words to show the impressions of the sender (Vaas, 2017).

The other proposed Emotion-Reading patent is Techniques for Emotion Detection and Content Delivery. It plans to own its path to the cameras on our phones, tablets, and laptops by observing us as we peer at the screens. Another Emotion-Gleaning technology has been described where one will generate emojis based on the user’s facial Expression. These types of technology tools can be used by the marketers to gauge the reaction of the consumers and cater to them (Vaas, 2017).

In short, Context-Aware Systems are made to adapt their systems in accordance to the context of the user without their active participation in such changes (Khattak, et al., 2014). The development of these Context-Aware Systems synthesizes the behaviour and environment of the user with an aim to ensure that such systems will continually be usable and effective throughout time (Baldauf, Dustdar, & Rosenberg, 2007; Khattak, et al., 2011; Chen, Nugent, & Wang, 2012).

Context-Aware Systems are becoming more popular and have been developed into diverse domains or interface such as Location-Based Systems (Want, Hopper, Falcão, & Gibbons, 1992), Context-Aware file system (Hess & Campbell, 2003), Context-Aware Security (Covington, Fogla, Zhiyuan, & Ahamad, 2002), Context-Aware Activity Recognition (Khattak, et al., 2011), Context Based Searching (Ding, et al., 2004; Khattak, Ahmad, Mustafa, Pervez, Latif, & Lee, 2013), and Intelligent Healthcare Systems (Khattak, Ahmad, Mustafa, Pervez, Latif, & Lee, 2013; Khattak, Pervez, Lee, & Lee, 2011; Hussain, et al., 2013; Khattak, Pervez, Han, Lee, & Nugent, 2012). Nowadays, the use of Context-Aware Systems has become commonplace and part of everyday life for users of the cyber world. In fact, Cyber Behaviour sensing and computing devices are known to have been already installed in most smart devices (Khattak, Ahmad, Mustafa, Pervez, Latif, & Lee, 2013; Khattak, Pervez, Lee, & Lee, 2011; Han, Vinh, Lee, & Lee, 2012).

The context gathered from the users is classified as internal or external (Hofer, Schwinger, Pichler, Leonhartsberger, & Altmann, 2013). But the quality of information derived by the Context-Aware Systems is not dependent on whether it is internal or external. Such systems are designed to acquire and synthesize context in order to make it useful and effective for further processing (Baldauf, Dustdar, & Rosenberg, 2007; Han, Vinh, Lee, & Lee, 2012).

Another domain of Context-Awareness is a personalised hypermedia application. It is a hypermedia system which, like any Context-Aware Systems, applies the information, structure and the physical attributes of the networked hypermedia objects to the user’s environment, characterization and behaviour. This Context-Aware domain is considered as an interactive system. This means that users are allowed to navigate a network of linked hypermedia objects. Examples of hypermedia are the web pages which contain various media types like text, photos, videos, clips, applications and other similar elements. (Kobsa, Koenemann, & Pohl, 2001)


User behaviour in the internet has become subject to breach of privacy and security. Smith et al. (1996) enumerated the four instances where the issue of privacy concerns arise, to wit: the gathering of personal information, unapproved indirect use of personal information, supplying of wrong personal information, and unauthorized access to personal data (Stewart & Segars, 2002). These concerns in online marketing are being applied in the same regards like collection of the personal information, storage and control of these information and observance of the privacy practices and use such data in a way that promotes marketing without breaching the sensitive line of privacy (Malhotra, Kim, & Agarwal, 2004). On the other hand, most consumers are concerned on the unapproved indirect use of data and the supplying of wrong personal information (Brown & Muchira, 2004). There will be a possibility that the consumer may lose his trust to the vendor when the latter insisted on getting the information evoking privacy concerns (Camp, 2003).

Google and Microsoft argue that it has the right to scan all emails passing through its systems. This means that Google can read keywords that can trigger relevant advertisements (Schofield, 2013). Facebook has a privacy setting to allow users to stop the collection of behavioural information. However by default this is set to allow the collection of private information. Even if one were to opt out, it does not stop advertisements on Facebook (Smith L. , 2016).

There are various instances that are possible to happen in terms of breaching of privacy with the utilization of Cyber Body Language . Context-Aware Systems are made smart and adaptable, mostly users are caught off guard, but their behavioural patterns are already studied in the furtherance of the systems they use. Most of the time, this Context-Aware devices are useful, but unauthorized access or misused of the data gathered from the user might post a security threat. Although there may be concerns that Context-Aware Systems can be very damaging to the privacy of the user, it should also consider that these Context-Aware Systems can also provide security. This way, the Context-Aware Systems can intelligently analyse the behaviour of the user, assess the possible breach of security and synthesize those information to strengthen the security systems.

According to Milne and Gordon (1993), the collection of such Personal Information called for the proper treatment as it is considered to be an “Implied Social Contract” with the consumer. The consumer has a right to sue and be entitled with compensation if there such an instance where his trust has been breached by the vendor (Solove, 2006). Because of this, the vendor is always required to ensure that he observed fair information practices to guarantee the consumer that his personal information is well-respected and well-preserved (Culnan, 2000; Dinev & Hart, 2006).


One of the domains of Context-Aware Services popularly applied is the location-based services. These services are usually present in mobile services that follow the location of their users (Rao & Minakakis, 2003) which basically the primary market of the Context-Awareness. One location-based services application widely used is the Geo-Fencing and also its allied services like a notification signal wherein it reminds user when it enters a certain area like a nearby police station or school grounds. (Namiot, 2013)

According to Rivero-Rodriguez et al. (2016), there can be issues or problems can arise from the inability to secure location privacy in an Location-Based Context-Aware environment One of the issues in location-enabled aware device is the spamming where the user is barraged by advertisements of the products or services from businesses. The second issue is the threat to personal safety of the user where he can be easily targeted of harassment, assault or any crime because his location is easily traced. The last issue is the ability of other users to access the spatio temporal information of a user where their Privacy, Personal Information, Religious and Political views are located. (Rivero-Rodriguez, Pileggi, & Nykänen, 2016)


Advertisements are targeted to users that meet certain behavioural characteristics. An example of this is the tool created by Cambridge University called “Apply Magic Sauce” which is said to predict the Psycho-Demographic profile of the user based on the footprints left on the social media like Twitter and Facebook. This is developed to give specific perception on the behaviour, personality, attitude, interest and level of interest of the user (Psychometric Centre of University of Cambridge, 2017).

Another tool called “Crystal” is also created to predict the profile of a user by analyzing the email history and LinkedIn profile of a user. This tool can also be used against the email contacts to analyse their behaviour for the user will have a perception of his contact’s behaviour or character. The main objective of this tool is for the user to become a good communicator (Crystal Project Inc., 2017).


The use of Cyber Body Language is a result of the evolutionary process of computing systems were user’s patterns and behaviors are studied to become the trigger points for enhancement, upgradation or replacement of systems installed. This adaptability mechanism of devices has been developed really well to read Cyber Body Languages that it became a source of concern for all. Since most users had already experienced how it can exploit, harass, bombard or sneak into their personal space where security and privacy is at great risk. However, it cannot be discounted that the utilization of Cyber Body Languages is a mine-field for discoveries that can help continuously upgrade and advance technologies without explicit participation from the users. Keeping in mind the age-old respect of one’s privacy and personal space, it is only logical to suggest that the development of Cyber Body Languages should be regulated.


  1. Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A Survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput , 263-277.
  2. Batty, M. (2003). The Next Big Thing: Surveillance from the Ground up. Environment and Planning B: Urban Analytics and City Science , 30 (3).
  3. Bolchini, C., Schreiber, F. A., & Tanca, L. (2007). A methodology for a very small database designs. Information Systems , 61-82.
  4. Brown, M., & Muchira, R. (2004). Investigating the Relationship between Internet Privacy Concerns and Online Purchase Behavior. Journal of Electronic Commerce Research , 62-70.
  5. Camp, L. J. (2003). Design for trust. In R. Falcone, Trust, Reputation and Security: Theories and Practice,. Springer-Verlang.
  6. Caplan, J., & Torpey, J. (2001). Documenting Individual Identity: The Development of State Practices in the Modern World. Princeton, NJ: Princeton University Press.
  7. Chen, L., Nugent, C., & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering , 961–974.
  8. Covington, M., Fogla, P., Zhiyuan, Z., & Ahamad, M. (2002). A context-aware security architecture for emerging applications. 18th Annual Computer Security Applications Conference, (pp. 249-258). Las Vegas, NV.
  9. Crystal Project Inc. (2017). Crystal. Retrieved May 15, 2017 from Crystal Knows:
  10. Culnan, M. J. (2000). Protecting Privacy Online: Is Self-Regulation Working? . Journal of Public Policy and Marketing , 20-26.
  11. Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing , 4-7.
  12. Dinev, T., & Hart, P. (2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research , 61-80.
  13. Ding, L., Finin, T., Joshi, A., Pan, R., Scott Cost, R., Peng, Y., et al. (2004). Swoogle: A search and metadata engine for the semantic web. 13th ACM International Conference on Information and Knowledge Management, , (pp. 8-13). Washington, DC.
  14. Han, M., Vinh, L., Lee, Y., & Lee, S. (2012). Comprehensive context recognizer based on multimodal sensors in a smartphone. Sensors , 12588–12605.
  15. Hess, C., & Campbell, R. (2003). An application of a context-aware file system. Personal and Ubiquitous Computing , 339–352.
  16. Hofer, T., Schwinger, W., Pichler, M., Leonhartsberger, G., & Altmann, J. (2013). Context-awareness on mobile devices-The hydrogen approach. 36th Annual Hawaii International Conference on System Sciences, (pp. 6-9). Big Island, HI, USA.
  17. Hussain, M., Khattak, A., Khan, W., Fatima, I., Amin, M., Pervez, Z., et al. (2013). Cloud-based Smart CDSS for chronic diseases. Health Technology , 153-175.
  18. Khattak, A. M., Akbar, N., Aazam, M., Ali, T., Khan, A. M., Jeon, S., et al. (2014). Context Representation and Fusion: Advancements and Opportunities. Sensors , 9628–9668.
  19. Khattak, A., Ahmad, N., Mustafa, J., Pervez, Z., Latif, K., & Lee, S. (2013). Context-aware Search in Dynamic Repositories of Digital Documents. 16th IEEE International Conference on Computational Science and Engineering (CSE 2013), (pp. 3-5). Sydney, Australia.
  20. Khattak, A., Pervez, Z., Han, M., Lee, S., & Nugent, C. (2012). DDSS: Dynamic decision support system for elderly. 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012), (pp. 20-22). Rome, Italy.
  21. Khattak, A., Pervez, Z., Lee, S., & Lee, Y. (2011). Intelligent healthcare service provisioning using ontology with low-level sensory data. KSII Transactions on Internet and Information Systems , 2016–2034.
  22. Khattak, A., Truc, P., Hung, L., Vinh, L., Dang, V., Guan, D., et al. (2011). Towards smart homes using low level sensory data. Sensors , 11581–11604.
  23. Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review , 111-155.
  24. Korpipää, P., Mäntyjärvi, J., Kela, J., Keränen, H., & Malm, E. J. (2003). Managing context information in mobile devices. IEEE Pervasive Computing , 42-51.
  25. Kwon, O. B. (2004). Modeling and generating context-aware agent-based applications with amended colored petri nets. Expert Systems with Applications , 609-621.
  26. Malhotra, N., Kim, S. S., & Agarwal, J. (2004). Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research , 336-355.
  27. Milne, G. R., & Gordon, M. E. (1993). Direct mail privacy-efficiency trade-offs within an implied social contract framework. Journal of Public Policy Marketing , 206–215.
  28. Namiot, D. (2013). GeoFence Services. International Journal of Open Information Technologies , 30-33.
  29. Oracle. (2014). Digital Body Language: Reading and Responding to Online Digital Body Behaviors. Digital Body Language Guide .
  30. Psychometric Centre of University of Cambridge. (2017). Facebook and Twitter Prediction. Retrieved May 15, 2017 from Psychometric Centre of University of Cambridge:
  31. Rao, B., & Minakakis, L. (2003). Evolution of Mobile Location-based Services. Commun. ACM , 61-65.
  32. Rivero-Rodriguez, A., Pileggi, P., & Nykänen, O. A. (2016). Mobile Context-Aware Systems: Technologies Resources and Applications. International Journal of Interactive Mobile Technologies , 25-32.
  33. Schofield, J. (2013, August 15). Is Gmail secure enough for my private emails? Retrieved 2017 from
  34. Smith, H. J., Milberg, S., & Burke, S. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly , 167-196.
  35. Smith, L. (2016, June 3). You Need to Update Your Facebook Privacy Settings — Again. Retrieved 2017 from
  36. Solove, D. J. (2006). A Taxonomy of Privacy. University of Pennsylvania Law Review , 477.
  37. Stewart, K. A., & Segars, A. H. (2002). An empirical examination of the concern for information privacy instrument. Information Systems Research , 36-49.
  38. Vaas, L. (2017, June 12). Facebook wants to feel your pain (and your joy). Retrieved 2017 from
  39. Want, R., Hopper, A., Falcão, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems , 91-102.
  40. Woods, S. (2009). Digital Body Language: Deciphering Customer Intentions in an Online World. Danville, CA: New Year Publishing.
  41. Zhu, F., Mutka, M. W., & Ni, L. M. (2005). Service discovery in pervasive computing environments. IEEE Pervasive Computing , 81-90.


Translate »