Weekly Response


In this post, I would like to respond briefly to Richard Hendrix’s Disintermediated Existence. The presence of internet creates public and private spaces for people to share experience, thoughts and emotions. It is truly the process of accumulating memories which are stored in digital form. People are free to participate the discussion about life and death, but meanwhile, they are creating their own memories, perspectives or personalities. In this process of disintermediation and de-aggregation of information, people are making their own “democratized creation” in various ways.

Another great example is Jonathan Harris’s The Whale Hunt. The entire week’s record constructed with photos can be a database itself. But through the narrator’s point of view and the clear time-line, it also tells a story and represents a collection of memories of many others. Does it objectively reflect what has happened during that week? Or at least, the point-of-view of the narrator? Since the representation of things happen in real life can be deemed as certain forms of productions, how do we define or differentiate what is genuine and true from what is embellished or even fabricated? It is certainly very interesting that someone brought this up in class today.

After all, how people interpret the information provided just like how they judge things around with their own values. This resonates with Ryan’s perspective of the social norm on the internet. The question that Ryan asks at the end is thought-provoking,

“Are the things you read on the internet really expressing both sides to the story or is the content inhibited by social norms?”

I also want to raise a question: do the things people put down to represent their perspectives online really represent what they are?

Picture: http://www.newbreedmarketing.com/blog/marketing/blog-ideas-besides-blogging

Incomplete data of students speaking up in class



Date No. of times that students spoke up No. of students that spoke up The most times a student spoke up
3-Feb 26 15 4
5-Feb 10 8 3
10-Feb 17 12 4
12-Feb 18 11 3
17-Feb 23 13 5
19-Feb 15 9 3
17-Mar 40 15 7

Since last time being an observer, I decided to continue with a set of data in the following classes. The set includes the number of times that students spoke up in total, the number of students who spoke up and the most times that a student spoke up in each class. I tried to separate asking questions, or very brief conversational responses from a legit speak up. Thus, in my definition of speaking up, it should be a student that explains concepts, demonstrates thinking or answers questions. Any conversation that involves very short sentences or clarifications of certain words is not counted.

However, the data is heavily influenced by many possible factors. For instance, the attendance of each class could affect all three types of data. Also, the content of the class could affect the data, such as the “fish bowl” on Feb. 10th. Since “fish bowl” is not normal speaking up in class, the discussions are not counted into the data. Also, on Mar. 17th, the topic involves Apple Watch in which many people have strong interest. The total times of students spoke up is considerably high.

Nevertheless, the most times of a student spoke up in class doesn’t vary much. An assumption is that people tend to not to speak when they think they have already spoken up enough many times. From the data, the limit seems to be around 4 or 5 for people that usually speak up.

The data is incomplete since the collecting process was not consistent. Also, the counting process is performed by only me. I could possibly miss one or two times while taking notes or listening to anything that had my attention.

Hashtags and Graphs

This post will focus on the post by Peter Saunders about the hashtags. I absolutely agree with the point that hashtags sorted various information into certain categories and “handle the dirty work of aggregating” for the users. It is really interesting that Peter points out the inefficiency caused by the misspelling of hashtags. If you really think about how hashtags work, you will keywords, or tags, used in some blog websites including WordPress work the exactly same way. If you put the network into graphs, you might be able to see tags as endpoints that link to the endpoints on the other side, which are the posts. It resonates with the other reading, “Graphs” from Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg.

For instance, on instagram, you post a photo with #dataculture. The hashtag serves as a hyperlink between the category and the photo and thus the photo is the endpoint. The other way around, if you search the hashtag #dataculture, the hashtag becomes an endpoint with leads to the category. Just like the graph shown below:


If you have multiple of photos with multiple hashtags, the graph will go more complicated. And then with a large user group, the graph goes even crazier. This is where Cloud technology came in.



To draw a conclusion for this response, the hashtags are certainly inefficient sometimes because of the misspelling, but, instead of creating different databases, the hashtags create multiple hyperlinks to sort information into categories. Comparatively, hashtags are very efficient.


Graphs credit to:




Big Data: positive impact or negative impact?


The argument becomes more and more contentious between customers actively aggregating data profile and being passively collected personal information. The Target’s case put this issue in a more controversial and extreme situation. Since Target’s effort to speculate women’s pregnancy reached a very deep level of personal privacy that most people wouldn’t feel comfortable being asked about by strangers. However, on the other side, while Target is trying to make profit and solicit customers, it is also bring much convenience and promotions to the targeted women customers. Just as many other e-service-based companies like Netflix and Google are doing, most of advertisements or movie recommendations are the result of Big Data analysis. In most cases, we all benefit from the service brought by Big Data technology.

What can also be a backfire other than privacy violation? As I have talked about in the data critique, the minority population might be neglected through the process of Big Data analysis. Every 5 years, American Census Bureau collects data from various aspects like family, work, and education. With the database, the bureau tries to make estimates which can apply to the entire population. However, from the estimates, we can easily recognize the minority groups from the majority. Thus, it is more likely the private companies would design their products for the majority based on the estimates. Is Big Data hurting the minority groups? What should we do to improve? Perhaps we can predict that in the future, Big Data is able to provide the basis for a perfect market which hurts no minorities.

Picture from: Singh, Tarry “Big Data Is The Future Of Digital Marketing” WordPress. July 26th 2014. Web. Feb. 17th 2015. (http://tarrysingh.com/2014/07/big-data-is-the-future-of-digital-marketing/ )

Data Collection for Week 4


  • Times people spoke up in class: 26
  • People who spoke up: 15
  • The most times an individual spoke up: 4
  • People who shook legs: 7
  • People wearing a shirt: 8
  • People who kept notes with pen/pencil: 7
  • People who yawned: 2
  • Petty actions:
    • Touch chin: 10
    • Touch nose: 4
    • Bite fingers: 4
    • Play with pen/pencil: 4
  • Group discussion:1


  • Times people spoke up in class: 10
  • People who spoke up: 8
  • The most times an individual spoke up: 3
  • People who shook legs: 9
  • People wearing a shirt: 7
  • People who kept notes with pen/pencil: 9
  • People who yawned: 5
  • People who brought laundry to the class: 1
  • Group discussion: 1

Plus: It might be really interesting if observers keep track of how many times people speak up and how many people speak up per week, and make a chart in the end. Just a suggestion!

Formidable Dataveillance

Dataveillance has been an ordinary technology that is mostly used to trace personal trading records or economic crimes. Lyon compares dataveillance with the ubiquitous surveillance in Nineteen Eighty-Four and I see a lot of aspects they have in common. Let’s assume a person uses credit card frequently in daily life. He pays the check in a restaurant, buys flowers for his date or refills his car on a vacation elsewhere. Anything he does can be retrieved from the data of time and location in the records. Although the surveillance system provides protection for the customers, “Big Brother” is indeed watching you. After all, the Panopticon is not so different from the ‘Dystopia’ of Nineteen Eighty-Four. They both involve“the theme of exploiting uncertainty as a means of controlling subordinates.” (62) My uncle works in the Department of Internet in China. He told me that within one minute, he could pull out all records of a person in one year, including phone call, bank deal, utility bill, and browsing history. It echoes Lyon’s perspective that ubiquitous electronic surveillance has become true and has profound impact in our daily life.

Beside what we often consider as surveillance like dataveillance which comes down upon us from high-level inspection organizations, media, entertainment industry and social media provides opportunity for everybody to be one of the inspectors, just like the Panopticon. Two weeks ago, a Chinese young actor who has become famous few years ago had an affair. One week after the news, he published a letter of apology on his personal Weibo account in which he said he almost collapsed mentally. The formidable pressure is from the fans and media. People have become the Big Brother who constantly monitor others through the eyes of internet.

The electronic surveillance is not only ubiquitous but also omniscient. While we were exploring Google’s Ngram Viewer, I couldn’t stop thinking about the Google advertisement. The Cloud Data technology enables Google to put on the commercials that most interest me. They make decision based on all activities bound to my Google account. Recently, the most popular Chinese mobile social networking app WeChat, operated by Tencent, decided to run commercials to its eleven hundred million users with Cloud Data technology.

3 Years since published; 1.12 thousand million users; 4.4 hundred million monthly active users; 20 languages; NO.1 social media.


WeChat: to know more, click here.


Lyon, David. “From Big Brother to the Electronic Panopticon.” The Electronic Eye: The Rise of Surveillance Society. Minneapolis: University of Minnesota, 1994. 57-79. Print.