The project aims to be a tool that will ultimately democratize the process for any journalist to look into different data layers and generate data-grounded hypotheses.
Check out the preliminary proof of concept: http://web.media.mit.edu/~pernghwa/urban_data_watch/index.html
For this assignment, I was thinking more on how a natural scheme could emerge from reading a newspaper article. For example, the recent news article on South Africa xenophobic killing incident would surely generate lots of discussion. However, how can we leverage on the urge that readers would naturally respond to provocative news contents? Therefore, I aim to focus on a mechanism and a tool that could help generate such responses.
A potential theory for readers to gain traction with regard to a particular story is decentralized discussion versus viral phenomenon. While viral propagation is goes virtually with fewer deep discussions around the topic, meaningful discussions are nevertheless key to initiating grassroots movements. Therefore, my scheme would involve decentralized movement where people would start their own social media campaign trying to involve their immediately connected community members to discuss. If the contacted members find the issues interesting, they may likewise pass on the conversation. The aim of this social campaign is not mainly to optimize total number of people involved, rather, it tries to optimize total of actively engaged online users who may transform into further engagements revolving a possible resolution of the issue.
The following link shows a crude outline of what may possibly be a solution. First, a quick walkthrough of the website:
0. One needs to login to Twitter (or other social media accounts) and verify with app credentials
1. One first chooses a news article of interest
2. She should search relevant terms on Twitter for key phrases of discussion
3. She then starts a timed social campaign where she initiates the discussion through retweeting a popular yet provocative tweet or posting one of her own trying to contextualize through some hashtags. Within the timeframe allotted, she should generate certain volume of discussion with the amount of audience that meets the minimum level of public attention (say for example, 10 followers with average of 500 followers for each user -> 5000 potential viewership on the topic).
Steps 0-3 can be iterated for different individuals. At the end, we can aggregate similar discussions into larger discussions, naturally leading to virality. This is essentially a bottom-up approach to decentralized civic journalism. The website tries to reflect the above line of thought.
* site only tested on Chrome with 2650×1600 sizing (unfortunately not dynamic layout), so viewing experience may be affected.
Crime is often associated regarded by how someone perceives the environment he or she lives in. There are often places in a city that are more dangerous than others. However, are these places somehow attributable to the infrastructure within the community?
One hypothesis is brightness in the surrounding environment. While not bidirectional, but in unsafe regions, dark areas are perceives as especially dangerous. To test this hypothesis, I use the Boston Police crime report data and map the list of crimes occurring during nighttime to streetlight deployment. While incident reports seem quite ubiquitous, the crime density still seems quite high and perceivable in regions that are less safe and with less light installations.
Further, divide the crime incident by type, we can observe a heavy tail in terms of crimes that are distant from the nearest lights, emphasizing the perceptive unsafe nature. For example, crime types such as vandalism, burglary, and forgery have clear heavy tail. It is interesting to see crimes like vigilante and violence also share this characteristic to certain degree. This quick study provides a unique perspective to how interventions that cause perceptive differences may be a potential way to thinking about solving current municipal issues.
Follow the link to see the visualization of the data analysis (Note: the page may take some time to load).
Recently in the tech world, the term deep learning is almost as big a buzzword as “big data” or “machine learning”. In spite to the hype in this emerging research area, a lot of tech news articles seem to be quite overly optimistic about the technology, so this explainer hopes to explain deep learning, its whereabout, recent trends, and what deep learning is not.
This presentation seeks to use graphical navigation, where each node presents a chunk of information. The node color denotes a related semantic topic group and connections suggest similarity between chunks of information. Upon hovering the nodes, the highlighted nodes are the suggested next chunks to read. Due to the time constraint, this explainer is done in a very crude-proof-of-concept way, but hopefully it should show the gist of design philosophy.
On March 16, 2015, Tzu Chi Foundation, one of Taiwan’s foremost humanitarian NGO, announced in a press conference to rescind its appeal on development project in one of Taipei’s nature reserve. Since the controversy saw its heated public debate since late-February, Tzu Chi has faced widespread public criticism, and more recently, accusations of financial activities that may violate regulations.
Interestingly, the issue has a long history dating back to 2005. In a decade of legal controversy, there was never as much public attention gathered previously. The interplay between the new city administration and social media clearly helped the event to develop.
In 1997, Tzu Chi purchased a land in Taipei’s Neihu reserve and planned to change the property name to social benefit property to accommodate recycling factory. However, due to the fear the construction may affect water drainage that create potential hazard, Tzu Chi faced strong opposition from environmental groups. Tzu Chi officially made appeal to Taipei City government in 2005 for environment safety review, and had been under review since.
As soon as Taipei City Mayor Ko was elected in December 2014, he openly expressed that he was against Tzu Chi’s development project appeals. In an interview on February 23, 2015, Mayor Ko criticized Tzu Chi was very “weird”. This immediately triggered a fierce online response.
Nun Shu’s initial response to Mayor Ko’s interview
Nun Tzu-Hui Shu, the spokesperson for Tzu Chi, wrote a post on Facebook denouncing Mayor Ko “returned evil for good” and his actions will ultimately suffer “divine consequences”. This remark went viral in Taiwanese social media. On PTT.cc, a bbs site that functions very similarly to Reddit, the discussions soared to an unprecedented level. Because of Tzu Chi’s religious background (it is founded and led by Buddhist nun), many more posts emerged accusing Tzu Chi acted very cult-like. As one user accused,
The founder recruited doctors not based on merit, but by how much they enact “appreciation” to the founder… once when the founder visited a hospital in Hsin-Chu, when she exited the car, the doctors would kneel as a sign of invitation…
With the growing discussion on social media, many public figures start to cast other doubts on Tzu Chi’s operation. University of Taipei’s urban planning professor Chung-Hsin Yang posted study on Tzu Chi’s property transaction record showing Tzu Chi would purchase industrial properties and rename for residential uses.
Scholars openly criticized Tzu Chi for violating zoning regulations
Amid the public debate, with Nun Shu trying to convince the public that Tzu Chi is abiding to regulations, rumors on social media became viral that show doubts on Tzu Chi’s organizational structure. One notable example was how Tzu Chi deploys a caste system where only people who have donated a certain amount of money can promote up the rank ladder. Some PTT.cc users started sharing a crystal sculpture of a Buddha sold by Tzu Chi costs more than $11,000 USD with suspected production cost of less than $1000 USD, which spurred an outrage in the PTT.cc community, prompting many users posting photoshoped mockups of the sculpture.
Social Media response to Tzu Chi’s crystal sculpture
With a sustained public attention on Tzu Chi’s controversy, people started to provide more cases indicating a potential scandal may be taking place. On Match 10, one PTT.cc user claiming to be an “insider” posted that another Tzu Chi’s development project in Taichung taking place at a historic site was undergoing construction without proper examination of potential damage to historic remains. Other PTT.cc users cast doubts on Tzu Chi’s money flow.
On March 16, Tzu Chi announced to rescind on the controversial appeal on Taipei’s development project, hoping to end the controversy. However, the hosts at one of the premiere politics talk shows quitted in response to Tzu Chi’s press conference, claiming that “Tzu Chi has controlled the media to show only the good sides”. The public onslaught ensued, with more users on PTT.cc producing accusations, claiming Tzu Chi’s portfolio includes foreign stocks, along with notorious companies such as Monsanto. Tzu Chi’s worry is now only a beginning.
Social Media Feedback Loop
In comparison to the past, the Tzu Chi controversy has gathered a lot more attention. It is interesting to see how there is a symbiotic growth between the discussions on PTT.cc and public figures’ Facebook posts. We can see there is a strong autocorrelation between number of posts and attentions on PTT.cc, and Facebook attentions. In the past, either of the two sources were as heavily used so people only chatted controversial topics locally. The figure demonstrates how social media acts as information amplifier.
A typical PTT.cc interface
Data Visualization: http://web.media.mit.edu/~pernghwa/tzuchiviz.html
Disclaimer: Due to my non-journalistic background, my media reading behavior should exhibit some substantial differences.
I usually have a good appetite for a variety of media contents. However, I am usually lazy to look through traditional news media rigorously to find good contents, so I resort to places like Facebook. Surprisingly, over eighty-six percent of posts are written by news-related accounts, which makes Facebook a source of news aggregator. The collection includes three streams of information: traditional organizations like BBC, CNN, Wall Street Journal, New York Times; technology-based websites like TechCrunch, Mashable, Popular Mechanics; Academic publications like Nature, Science, and Technology Review (I mainly read their daily arXiv paper selection). Another source of information on Facebook is the Chinese articles shared by friends. There are several news websites (CNN, Time, WSJ, Bloomberg) that I regularly browse through for important articles. Technology and other miscellaneous websites include those technical articles that I simply enjoy reading in my spare time. I am also subscribed to several mailing lists and I regularly receive news update links, as well as recommending Quora and LinkedIn articles.
One particular information source is PTT. This is a very popular Taiwanese bulletin board system site that has very high user traffic in the age group (~100,000 simultaneous login accounts). There are many “forum”-like boards for posting articles and commentaries. I especially like this media outlet because it is void of algorithmic manipulation, where visibility is only determined by popularity in responses. Although the end result is the sheer amount of noisy contents, I still use it as the main source of Chinese news articles.
In terms of viewing behavior over the week, I tend to read more technical articles during the week, with increasing readership on Facebook and PTT articles. My weekday reading schedule is focused on three time slots- morning, noon, and midnight. For weekends, I indulge myself to greater schedule flexibility to read whenever I feel like reading.
It may seem PTT dominates the amount of articles I read. However, there are some subtle differences in my attention of article browsing. Usually I have a much higher impression of article volume on sources like PTT and Facebook, so a good amount of title summarizations are available at first look. When I click the links, I often just want to quickly expand on the news title to have the overall picture of the story. I do it differently when browsing news and technology-related websites, heavily investing my brain power concurrently thinking and reasoning during reading.
PTT is a rich source of Chinese news articles with sparse expert response. However, due to the bulletin format, contents are easily flushed from user view. Out of 821 articles posted on February 17th, only 134 gained substantial popularity, with merely 5 articles containing interesting content.
I also experimented with my Facebook wall for the date of February 17 and collect the list of accounts whose posts appeared on the wall. I observe the total times the post appeared on my wall and how often I would prefer the link content. Based on the plot of impression number against click through probability, friends that post more often tend to deliver interesting contents. This shows Facebook knows not to bother me with users who post too often for users that are both active and informative. Not so with news accounts: my wall is bombarded with irrelevant articles. At least in my case, Facebook’s algorithm is too conservative to suggest articles on a topical basis.
