RAA: Applying “the power of the ask” in social media website?

RAA stands for: Research Article Analysis

Paper discussed:

Wash, R., & Lampe, C. (2012). The power of the ask in social media. Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, CSCW  ’12 (pp. 1187–1190). New York, NY, USA: ACM. doi:10.1145/2145204.2145381

I’ve always wondered what motivates people to post comments on social media websites. Contributing, fun, or self-presentation? Probably most of readers are just like me: I usually felt lazy and seldom intensively involved in online discussions. This CSCW2012 paper came from Dr. Cliff Lampe at University of Michigan, trying to apply “the power of the ask” to promote more comments on social media websites. Let’s see if he can achieve this goal.

1. Purpose of the research:

Test a UI design grounded on “the power of the ask” strategy in philanthropy to see if it can induce users to contribute on a social media website.

The foundation of this research goal is that “charities and social media systems are both instances of what economists call public goods”. Voluntary contributors are needed but it is always hard to motivate people to become one. The authors claimed these two systems face two similar issues that prohibit people to contribute:

• Which websites/charity organization to contribute to?
• When should this contribution happen? Procrastination happens and stops them from contributing later.

The power of the ask is a powerful fundraising method widely used in charitable fundraising to solve these issues: upon asking to donate explicitly, people can react to the request, donate money immediately (when) to the person sent out request (to whom). Thus, the authors would like to apply this method to social media website, based on the similar nature of these two systems.

2. Methods:

The authors carried out a randomized field experiment on an existing social media system: the Great Lakes Echo, which is a WordPress based news service run by the Knight Center for Environmental Journalism. During the experiment period (10 weeks), users were randomly assigned to 3 conditions: no ask, immediate ask, and reminder. No ask provides the default interface as we can see in a WordPress blog, with comments and commenting textbox at the end of the article. Immediate ask and reminder conditions both provided popup windows 500ms after the page was full loaded, with two buttons: No Thanks, and Leave a Comment. The difference is that the immediate ask conditions provided a commenting box for readers to comment immediately, while the reminder condition asked readers to comment after reading the article. If you click “Leave a Comment” in the reminder condition, the page will automatically scrolled down to the comments area.

A reader is assigned randomly to only one of the conditions and it will be kept in the browser cookie so that he/she would always encounter the same condition during the experimental period. Also, one can only see comments posted by other users under the same experimental condition.

3. Main Findings:

A total of 266 comments were generated during this 10-week period.

• No ask and immediate ask conditions performed similarly, with 83 and 81 comments generated respectively. Reminder condition had higher comments: 102.
• There is a dropoff in the effectiveness of the popups over time, and 3 conditions are converging to approximately the same number of comments on average.
• Popups didn’t promote the quality of comments.

4. Take Aways:

I like this article in the way that it borrows idea from another area reasonably and tested with a field study, which is quite interesting to read. However, I found several pitfalls (in my view), which I think compromised the study results.

• The popup windows were shown 500ms after the article was fully loaded. The authors did a good argument on why other solution didn’t work and this one is the most clean one and it is worth to try “at the expense of some amount of external validity”. However, if you could imagine, at the time the window pops up, most readers must have just started reading a little bit, which basically made the “immediate ask” condition useless: who can give a comment when he/she just starts reading? So I’ve expected the result that “no ask” and “immediate ask” would make no difference. I am curious at what percentage of users clicked “No thanks” button under this condition? This was not reported in the paper. Similarly, I am curious to know what percentage of users clicked “No thanks” under the condition of “reminder”.
• In the result part, it was claimed that 179 out of 209 commenters only contributed a single comment during the study, which could almost rule out the possibility that a single individual contributed enough comments to alter the results. However, it was unclear whether those 30 commenters were uniformly distributed in 3 conditions. With only 206 comments, if most of these 30 commenters who intend to post more comments were happen to aggregate in a certain condition, it would bias the result a lot.

RAA: A recent study on credibility of tweets

RAA stands for: Research Article Analysis

Paper discussed:

Morris, M. R., Counts, S., Roseway, A., Hoff, A., & Schwarz, J. (2012). Tweeting is believing?: understanding microblog credibility perceptions. Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, CSCW  ’12 (pp. 441–450). New York, NY, USA: ACM. doi:10.1145/2145204.2145274

As I was doing a class paper regarding use of Twitter and self-presentation on Twitter, I found this newly published article quite interesting. In the age of information explosion, people rely more and more on personalized information channels with fast information updates to feed themselves with fresh news. Twitter, combines with multiple searching platforms, becomes ideal medium to provide useful information. Meanwhile, credibility issue rises up as people consume more and more tweets. This study took a look into elements that affect tweets credibility.

1. Purpose of the research:

Understand features that affect readers perceived credibility of tweets.

2. Methods:

A mix of survey and experimental studies were conducted to achieve the research goal. Survey was firstly used to gain the general perceptions of Twitter users on tweets credibility. Experimental designs were carried out later to focus on testing 3 core and most visible features (message topics, user names, and user images) reflected from survey results.

3. Main Findings:

People were poor at judging the truthfulness of tweets based on contents alone; instead, they inclined to use available heuristics, such as user names and user images to assess credibility of a tweet. For example, a default Twitter user image decreased the tweet contents credibility as well as author credibility, while a topically related user name (e.g., LabReport) increased credibility compared to an internet name (e.g., Pickles_92). These findings had great implications to both individual Twitter users who want to enhance their credibilities, and UI designs of search engines, which also has desire to increase perceived credibility of searching results.

4. Take Aways:

Besides the research finding itself, there are 2 points that I found interesting and useful for my future research:

(1) A very clear and persuasive background section

This paper provided a very clear and strong argument for the need of the study. The background regarding credibility study on Twitter was mainly composed with 3 parts:

• Concerns about credibility do exist, but no one studied what features contribute to it. – served as a gap needs to be filled.
• A study about Twitter user name existed, which studied the relationship between user name and tweets’ level of interestingness. – served as a step-stone that this research can build upon.
• There are systems to automatically / semi-automatically classify tweets credibility through combination of crowdsourcsing and machine learning. — served as an application which this research can help with.

These 3 arguments triangulate each other, building a solid ground to claim the desire and value of this study.

(2) Snowball sampling in social computing research

In the experimental study part, the authors claimed that recruiting participants through advertising to their own followers was undesirable, due to the drawback of snowballing sampling strategy. This gave rise to my curiosity since though I knew the definition of snowballing sampling strategy, I never use it before and I didn’t know its drawbacks either. I referred to the citation the authors gave here, which is [Bernstein, M. S., Ackerman, M. S., Chi, E. H., & Miller, R. C. (2011). The trouble with social computing systems research. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems, CHI EA  ’11 (pp. 389–398).]. In this CHI 2011 paper, the authors gave some theoretical framework to help with social computing system research. Regarding snowballing sampling strategy, this paper actually acknowledged the weakness of it as “the first participants will have a strong impact on the sample, introducing systematic and unpredictable bias into the results”. However, the main point of this paper was to suggest researchers to embrace snowballing sampling as it is “inevitable” due to 3 reasons:

• The nature of social computing is: information spreads through social channels.
• Random sampling is an impossible standard for social computing research because influential users exist to bias the sample.
• Many social computing platforms are beyond the researcher’s ability to recruit random sample.

Thus, we might be able to acknowledge that snowballing is not an ideal sampling strategy but inevitable in some sense in CHI research. We should fully aware of its danger of bringing in biased sample, and use it wisely. In this credibility paper, the authors recruited participants from Microsoft and Carnegie Mellon University, which are the organizations they belonged to. This sample do include some degree of diversity but also has its own bias. As the authors pointed out, some other demographics that consume tweets were not covered by this recruitment method. Overall, a biased sampling might be inevitable for social computing research, it is the researchers’ call to choose from different sampling methods based on their research questions and maximumly minimize the bias in terms of answering their research questions.

Reading notes about Lean UX

I came across the concept of “Lean UX” from a Chinese UX blog, talking about how to simplify and cut unnecessary UX design process to face the rapid updating market. The traditional UX development, as we learned from most courses, are recognized as deliverable-based process. UX researchers/designers are supposed to render different kinds of deliverables. However, the whole process with fine reports requires a relative long period (typically several months) to define the requirements of the products. This would be a great risk for IT companies nowadays, putting them in a position that the product might already out of date when developed. Also, there is a great waste of time and deliverables that could not be directly turned into final experience. To solve the problem, Lean UX was brought up with following features:

1. Cut completed documentations to bare components necessary for implementation.
2. Split long design process into short, iterative, and low-fidelity cycles; gather team-wide suggestions during iterative cycles.
3. Stop pushing pixels, pick up whiteboards, pencils, papers, or even napkins to convey early ideas of workflows.

There are several benefits of Lean UX: the entire team could get more involved into the design process, and gain the sense of owner through this process; stakeholder could get more exposed in an early stage; cost is low for improvement and redesign. Drawbacks of Lean UX are obvious: designers might loose control of the design through the iterative cycles, with constant input from the entire team. This requires UX designers have big visions of the products to hold or approve different suggestions.

Article read: Jeff Gothelf’s post on Smashing Magazine.

Control Sensitivity in Design

I run into the problem everyday: when I try to adjust the temperature of my shower, it is just way too sensitive. I have to move the lever as carefully as I can, however, it still surprises me with sudden change of temperature. I end up taking a shower either too cold or too hot.

This occurs to me today because I read about “control-display ratio” today when I went over the human factor class that I am taking this semester. Control-display (C/D) ratio is measuring the change of control compared to the change of display (or outcome, response). If the C/D ratio is high, then the control sensitivity is low, and vise versa. C/D ratio is very important for HCI design as well. Low C/D ratio (high sensitivity) could save time when users are approaching the target, while high C/D ratio (low sensitivity) could help user with fine adjustment when they reach the target area. So a carefully designed C/D ratio is important for improving user controlling experience. For example, the movement of mouse or controlling stick should have a reasonable C/D control so that users could minimize the effort when they try to move their mouse to reach a target.

The following is a common example in our everyday life: when you try to adjust the window, cursor has to be moved within a narrow area in order to change the shape. If the control sensitivity is too high, it will be very very annoying.

Connect the Digital World with the Physical World II: SixthSense by Pranav Mistry

I posted a blog sharing the idea of connecting digital world with physical world days ago. Today, I would like to extend that topic a bit, with this AMAZING talk, also from TED. Pranav Mistry, a PhD student from MIT Media Lab, demoed his invention called SixthSense with us. The behind idea is to seamlessly connected digital world with physical world, making the interaction more intuitive, and the way he fulfilled it is simple, clean, and yet brilliant.

I am sure you will say “wow” after watching this. Enjoy!

Help Users to Reach “Inner Peace”

If you are a fan of Kung Fu Panda as me, you would certainly remember the magic power of “inner peace”, as the quote from Shifu: “Anything is possible with inner peace”. In my understanding, this “inner peace” is much like the word “flow” used in psychology, which means the mental state of intense immersion and organized focus. However, with emerging high-tech devices and over-loading information, we are losing control of our flow easily nowadays.

I would like to share with you a beautiful article written by Andrew Maier, who is a co-founder of UXBooth. In this post, Andrew commented on several efforts made by Apple to reduce noise and encourage users’ flow. This article itself is acting as a fabulous flow, from weaving personal stories to design. After reading it, I confirmed my thoughts about simple and clean design: reducing noise in design is not only an advance in tech development, but also shows care for human being’s life. I almost can see the picture that people were used to concentrate on reading centuries ago, getting lost in recent ten years with all kinds of distractions by high-tech products, and finally return to a quiet, calm working environment through good designs that try to reduce noise.

Connect the Digital World with the Physical World

How “human” can your mobile phone be? If you are thinking Siri as an example, check this TED talk out, it will surprise you.

This short video made me smile: I will definitely spend more time with my mobile phone if it is that sweet! The whole idea of improving hand held device nowadays is not only making them more reliable, faster, or smaller, but making them more user-friendly. So ultimately, how friendly can a device be? This TED talk pointed out a very good direction for us: bring digital experience more close to our physical experience. Just like you can now speak to Siri than just navigate through pushing some buttons, or you could flip over pages when you are reading books on iPad. Though the ideas in this talk are not so practical or necessary in some peoples’ eyes, I believe it is a right direction for UI designers to pursue: making the interactions with technology more intuitive.

However, another question we need to address at the same time is, to what degree should the digital world resembles the physical world in order to not be considered as “backwardness”? As we’ve adapted ourselves with the button “language” of digital world, even we’ve been enjoying the “high-class” sensation when we use the superb multi-touch screen, do we really want the feeling of the thickness of the books back?

I guess there is a lot of user research need to be done before any fancy new designs being carried out. Overall, making the technology more intuitive, more “human”, and more considerate in a clever way is definitely the future of digital tech design.

A Brilliant Way to Access the Historical Trend

I came across this interesting talk on TED today. Some Harvard and Google researchers used the data from Google Books to pull out 500 million words from 5 million books across the centuries, created an online interactive tool. Using this tool, you could take a glance of the trend of human culture history. They gave some really interesting examples with wise interpretations in this talk.

I love the idea of being able to grasp the trend of certain topic quickly. This inspiring me of making similar application using data from different area. For example, if data is pulled out from major social media, we probably could see Steven Jobs appeared with higher frequency among Oct. 5th, 2011, with a positive correlation of appearance of iPhone 4S. If data is pulled out from some major forum and blogs of interaction design, trends of using/referring of certain usability methods could be gained.

RAA: A Practical Open Card Sorting Study

RAA stands for: Research Article Analysis

Paper discussed:

Lewis, K. M., & Hepburn, P. (2010). Open card sorting and factor analysis: a usability case study. Electronic Library, The, 28(3), 401-416. doi:10.1108/02640471011051981

1. Purpose of the research:

This paper describes the process, analysis, results, and implications of a card sorting usability study conducted for redesigning the library Website of University of Illinois at Chicago.

2. Methods:

Totally 18 participants were recruited and 15 were completed the task at the end. Among these 15 participants, 7 are undergraduate students, 7 are graduate students, and 1 is faculty member.

The open card sorting was done individually with each participant.

• The researchers created 93 numbered index cards. Label on each card was one of the existing or potential content from the library website.
• The participants were allowed to create anything missing or duplicate cards where they felt the cards belonged to multiple categories, using blank index cards.
• The participants were also allowed to discard cards that they felt redundant or named a category with “other”  or “general” for cards seems necessary but not fit into any other categories.
• Speak aloud protocol was carried out to gain the participants’ rational for sorting decisions.
• The researchers didn’t answer any question to define the label terminology or indicate which category should a card go.

Post-analysis using “factor analysis” was done to indicate the association of a card with a category.
3. Main Findings:

Using the methods above, the researchers got the final result of sorting these 93 cards into 11 categories, with 27 cards not sorted into any categories. Besides the final sorting result, the researchers also concluded some qualitative findings as follows:

• Participants tend to group together cards that have the same words on them.
• Participants were sorting not only by format but by processes or tasks.
• The 27 cards that were not associated to any categories were due to different reasons: should be piled to themselves; redundancy; vague meaning; meaninglessness.

4. Analysis:

I found this paper particularly interesting because the up coming card sorting project. This 2010 paper not only described the project process and findings, but also did a good job reviewing previous card sorting study done by several university libraries. The qualitative findings of this paper reminds me of some essentials of designing card sorting experiment, mentioned by Gergle & Wood (2002), such as “listen to other comments about the content” and “include a brief description on each card”. Overall, it is a well-written paper with considerable details and discussions, which could be used as a good reference to our project. What I am going to dig deeper is their statistical method to combine the results. This seems to make much more sense than just eyeballing the results. I will write about different methods of combining card sorting results in a future post.

 

References:

Gergle & Wood (2002), Usability for the Web: Designing Websites that Work. Morgan Kaufman.

Travel with Flexible Dates: Flight Search Should Help You

The more I browse other flight booking websites, the more I like StudentUniverse.

People always have the chance to travel with flexible dates: say, you have a 7-day Spring break, you aren’t really strict about which day to depart and which day to return; rather, the flight cost often plays a big role in helping you to decide the traveling date, since it varies depending on different dates.

When searching for flights, if you check the “my travel dates are flexible” box, StudentUniverse wisely offers a 7×7 metrics of prices, corresponding to different combinations of departure and return days that are within 3 days of your pre-set dates. In this metrics, pre-set departure and return date combination is in the middle, highlighted with dark blue color; and the cheapest prices are also highlighted with light blue color, trying to catch attention. With this metrics, you could easily decide which combination fits you the best, in terms of both price and schedule.

Compared to StudentUniverse, other traveling agents’ websites are not so considerate over the flexible traveling needs. They only provide the results of exact dates you offered in the search engine. Want to check adjacent dates to see if there are cheaper options? You really need to work hard: each cell in the above metrics means one round of search(change departing and/or returning dates and search again), plus needs to jot down search history so that you don’t forget after so many rounds. User-centered design is small, but it is everywhere.