CAN-CWIC 2016 Conference/Celebration Event

On January 22nd and 23rd, I was privileged to attend the Canadian Celebration of Women In Computing conference (CAN-CWIC). It was held in Ottawa with over 500 attendees. There were talks from  top industry experts and professionals, a recruitment fair, several hands on training sessions and a poster presentation. I took part in several events and I feel so lucky to have attended. And yeah, I presented a poster on my ongoing research. 

There was a lot to learn, but for me the most important were these three:

1) Be Visible. 

No matter where you are; at a conference with fellow researchers, in the department at school or in a meeting at work, be visible. Let people remember you for something positive, especially people meeting you for the first time.

2) Be Prepared

Same as the Boy's Scout motto, one should always be prepared. For example, I knew there was going to be a recruitment fair at the conference. Though I had sent in my CV earlier to the CV bank, I didn't go with any hard copy, I wasn't prepared. During the career fair, I was interviewed by 2 firms. One of the firms after speaking with me asked for my CV. I didn't have it on me, I felt silly. I wasn't prepared. In addition, it's always good to have a 3-minute elevator speech prepared in case the opportunity arises. For me, at a conference like this, most people wanted to know about me and my research. I have a standard 2-3 minute speech that covers that and it sure came in handy! For every event one plans to attend, it helps to have a 2-3 minute speech that describes you and your achievement especially if you're looking for some kind of favor at the event (a job, maybe).

3) Network, network, network!

See every event as an opportunity to network and meet people. One of the presenters said she doesn't shortlist people for interviews and subsequent employment based on the hundreds of CVs emailed to her daily. She employs based on recommendations from her networks. So it's very important to always network when the opportunity arises. (There is a difference between networking and bothering people.). Be visible on LinkedIn and build a solid network there. A colleague of mine just got a job in a top software development company in town. He got the job through a member of his network.

There were lots of other points raised but these stood out.

I'm thankful to the organizers of the conference who lodged us for free and provided us a travel bursary for our travel expense.

Persuasion and Persuasive Technology

Persuasion

Humans generally try to influence the behaviors of others through physical and non-physical means [1]. Coercion and persuasion are types of influence. While coercion is seen to be unethical, persuasion on the other hand is mainly considered morally justifiable and ethical. Persuasion is defined as influencing the opinions or actions of others without the use of coercion [2]. 

Persuasive Technology (PT)

Persuasive technology is the use of technology in persuasion.  B.J. Fogg defines PT as an interactive form of technology designed to influence people’s attitudes or behaviors [3]. According to Oinas-Kukkonen et al [4], PT is any form of technology designed to bring about change in one’s attitude or behaviors without the use of coercion or deception. PT can take several forms like e-commerce websites like Amazon that persuades users to buy more products, fitness bands like fitbit that encourages users to maintain an active and healthy lifestyle, and marketing products that drive the sale of goods. 

There are several advantages of persuasive technology over traditional media and human persuaders. Persuasive technology is more interactive than traditional media like radio ads and print. Research has shown that the more interactive a persuasive technique is, the more effective it becomes [3]. In terms of human persuaders and PT, human persuaders are not as persistent as PT. For example, Ebay persistently sends emails to users after a purchase for the user to rate the product bought. Ebay uses these ratings to generate personalized recommendations to users. In addition, using technology to persuade users, information can be presented in various forms including text, graphics, animation, etc. based on the user’s preference [5]. This is not possible with human persuasion. Finally, compared to humans, computer applications are ubiquitous. They can be used where human persuaders are not welcome and cannot exist [3].

Ethical implications of persuasion/persuasive technology

There is a thin line between persuasion and coercion. Repeatedly asking me to carry out an action because it will be beneficial to you could be termed coercion. So where does persuasion end and coercion begin?

Persuasive technology is currently a very active research area. I am presently looking into how PT can enhance sales in e-commerce businesses. I'm looking into how PT can enhance participation in social networks since most e-businesses that thrive have an active social network component. I'm welcome to comments, suggestions and possible collaboration.

References

[1] P. Powers, "Persuasion and Coercion: A Critical Review of Philosophical and Empirical Approaches," HEC Forum, vol. 19, pp. 125-143, 06/01, 2007.
[2] H. W. Simons and J. Jones, Persuasion in Society. Taylor & Francis, 2011.
[3] B. J. Fogg, "Persuasive technology: using computers to change what we think and do," Ubiquity, vol. 2002, pp. 5, 2002.
[4] H. Oinas-Kukkonen and M. Harjumaa, "Towards deeper understanding of persuasion in software and information systems," in Advances in Computer-Human Interaction, 2008 First International Conference On, 2008, pp. 200-205.
[5] W. C. King, M. Marie Dent and E. W. Miles, "The persuasive effect of graphics in computer-mediated communication," Comput. Hum. Behav., vol. 7, pp. 269-279, 01/01, 1991.

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Predicting Churn Of Expert Respondents In Social networks

As my research covers the use of social networks and persuasive technology to improve e-commerce businesses, I wrote a paper on predicting churn of expert respondents in social networks: a case study of StackOverflow. The idea behind this was three fold: 1)identify the experts in a typical Q&A network who answer most of the questions in order to keep the network active. 2)predict the probability of them churning (if they'll remain with the network or will leave) 3) suggest and implement persuasive technology that can successfully influence possible churners in the network and make them stay(work in progress).

The paper was accepted at the 14th IEEE International Conference on Machine Learning and Applications. Yours truly was at the sunshine state to present the paper.

The abstract is below.

In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77.