Article: “Visualizing Email Content: Portraying Relationships from Conversational Histories”

Authors: Fernanda B. Viégas, Scott Golder, Judith Donath

Summary

The authors of this paper developed Themail a tool that parsed user email correspondences and displayed yearly and monthly keywords. They implemented two modes “haystack” and “needle”. Haystack mode displayed words on the screen that showed more general, big picture themes and trends. Needle mode picked out more minute detail. Most users preferred haystack mode, specifically reflecting on their discourses with family and loved ones. One of their methods included taking special measures to isolate and consolidate correspondences with specific individuals. That is, they made sure that if a user had several email addresses associated “John Smith”, the addresses were consolidated into a single contact.

Reflection

Themail is and interesting social tool. One might use it like a photo album or an evolving time capsule. It would be interesting to integrate other media and info such as photos, gifs, videos, location crossovers, routes, etc.

Questions

What would be the effect of “facebooking” this tool such that users were exposed only to correspondences that they were more positively engaged in?

Another leaf out of facebook – might users enjoy a video-graphic memoir of their meaningful correspondences?

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Reading : “The Language that Gets People to Give: Phrases that Predict Success on Kickstarter”

Authors: Tanushree Mitra, Eric Gilbert

Summary

Mitra and Gilbert discuss the general subject, and prior research on crowd funding before elaborating on their own work. Crowd funding is when a business or project proposal uses a website to seek start up funds. Kickstarter is one such website and it does not finalize any monetary pledges until it the funding goal is met, by a deadline. Restricting their list to projects since July 2012 that were past their fund date, the authors had 45, 810 projects in their dataset. They used BeautifulSoup to scrap the html of these projects and followed a conventional bag of words model focusing on relatively common English words. Their model had over 20,000 phrases to use as predictive features.

After making their model, they used penalized logistic regression, which guards against co-linearity and sparsity by moving the co-linear coefficient’s weight to the most predictive feature. cv.glmnet, is the R implementation of this method that they used, because it also handles sparsity. Both sparsity and co-linearity were present in their data.

Their results are beautifully displayed in a figure with two phrase trees. One is of “funded” phrases that started with “pledgers will [receive, be, also, have, …]”. The other is of “not funded” phrases that started with “even a dollar [short, will, can, helps, …]”. The “funded” phrases showed man common elements, such as reciprocity (the tendency to return a favor after receiving one), and scarcity (limited supply of rare or distinct products that hold more value to pledgers).

Reflection

This makes me want to “hack” Kickstarter and make a fortune.

I read this one carefully because some of the tools and methods that the authors used are analogous to what my team would probably have to use on our project. It is interesting that the results break down into reciprocity and “money groveling”, respectively, as very strong positive and negative indicators of achieving funding. It is not especially surprising though, because it shows that pledgers have their own self interest in mind even when they are donating.

Certain markets might succeed much better in crowd funding that others. The target market has to a) have access to the internet and b) have enough extra money to bother looking at Kickstarter and c) have the time and means and interest to make use of any Kickstarter product. The successful pledge earning language in those already niche markets could be pretty different for each of the categories and sub-categories. It would be interesting to run a similar sub-analysis within the sub-markets. The authors poked into this by adding control variables, but it would be interesting to dig deeper.

Questions

Are there penalties for “over promising?” That is, if a project achieves a funding goal, it receives the funds. What if a founders of a project just kept the money and didn’t deliver anything?

Whatever happened to Ninja Baseball? That sounds awesome.

Bringing the qualitative to the quantitative is always a pain. How reliable are sentiment analysis tools? Can they be used to detect opinion?

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Summary

“Antisocial Behavior in Online Discussion Communities”

This article discusses the background, methods, and results of analyzing the posting habits of trolls (anti-social online community members). They categorized users as FBU’s (Future Blocked Users) and NBU’s (Never Blocked Users) and compared and contrasted their behaviors. They further divided the FBU’s according to low and high post deletion rates. After determining some of the patterns of trolls, they attempted to predict whether users would be blocked in the future based in their first few posts and design a means to traffic trolls away from or out of online communities. All or most of the results of their analysis were close to what they expected, showing grounds and plausibility of designing against anti-social users.

Reflection

This is an important step in the development of the online social network. As entertaining and common place as trolling is for some bored, sad, members of society, it has no place in peace and progress. If it has any place it is to show that it ought to be policed. The internet is a bottomless pit of distractions and an infinite pool of people to take advantage of. Troll trafficking might lead to a more wholesome and effectively developmental online community.

Questions

Is there really a way to completely safeguard against trolls? Won’t the determined, bored middle school student find a way?

Does the increase of anti-social behavior when transposing the interaction to online platforms reveal something inherent about society or humanity?

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Summary

“Identity and Deception in the Virtual Community”:

This article modeled online/virtual identity with the same model used one plants and animals. It focused on Usenet as an example of a virtual community. It focused on handicap signals, assessment signals and conventional signals as a means of determining the validity of online identities. It distinguished between person and persona and emphasized the weight of voice and style in determining and establishing virtual identity.

“4Chan and /b/”

This article examined the concept of identity and voice on another platform. Examining /b/, the random chat board of 4Chan, it discerned the influence of mass, chaotic, anonymous communication on a platform where threads get squeezed out as soon as there is not enough interest in them. Essentially asking the questions “What makes a subject last?” and “How does anonymity effect content?”

Reflections

One interesting note from the first article was that it is “only a matter of time until exclusive on-line addresses become symbols of status.” The phrase “bad and boujee” would then evolve to “tagged as boujee.”

The concept of voice and style being more powerful than a name or a face when establishing identity is much older than the internet. But it is not older than virtual communities, depending on what you consider “virtual”.

4Chan/b/ is a headache, but it serves as an interesting display of what holds people’s attention and what they are willing to share under anonymous circumstances. As menacingly chaotic as it is, there are valuable insights to common questions of humanity (within the scope of humans who use the cite).

Questions

In the 4Chan study, did they isolate EST users or just like at the peaks in EST?

How many unique personas can a single person have before they go completely insane?

How old is the concept of a virtual reality? What is the oldest implementation?

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Naaman, Mor, Jeffrey Boase, Chih-Hui Lai. “Is it really about me? Message Content in Social Awareness Streams.” ACM Digital Library, ACM, dl.acm.org/citation.cfm?id=1718953. Accessed 29 Aug. 2017.

Akshay Java, Xiaodan Song, Tim Finin, Belle Tseng. “Why We Twitter: Understanding Microblogging Usage and Communities”. http://aisl.umbc.edu/resources/369.pdf. Accessed 29 Aug. 2017.

Summary

“Is It Really About Me?” is an analytical research paper attempting to determine classifications of Twitter users. The classifications found were “Informers”, users who share a lot of non-personal information, and “Meformers”, users who share a lot of personal information. The researchers were able to cluster users into these groups with strong statistical significance. They did this by first breaking down user posts into different categories (Information Sharing, Me Now, Opinions / Complaints, Random Thoughts, etc.) and then clustering users based on number of occurrences of those categories of posts.

“Why We Twitter” is another analytical research paper with a broader goal of observing some topological and geographical aspects of Twitter as an example of microblogging. They showed special interest in the content shared by different communities and the inter/intra-community  connections and patterns. They found that North America (followed by other industrialized continents) is responsible for most Twitter activity. The also found that the content of posts changed through the week, with “school” and “work” being drowned out by “party” and “friends” approaching and during the weekend. The researchers found categories of user posts including: Daily Chatter, Conversations, Sharing Information, Reporting News. These resulted in classifications of users including: Information Source, Friends, and Information Seeker.

Reflection

It is interesting to note the similarities in analysis results and interpretation between the two studies, despite the difference and intentions and methods. Specifically, the categorization of posts leading to the classification of users. Both studies ended up picking out a similar “informer” class; “friends” and “meformers” seem to have largely the same posting habits; “Why We Twitter” put special emphasis on users who did not create much if any original content. It is fascinating to look at these studies and the patterns that they detected. Twitter is a great source of digital data produced by the living organisms that it is about.

Questions

• How else could user posts be categorized?
• Does the intention of a study have a strong effect on the interpretation of the data?
• What would the results of blind machine learning look like if it were also to categorize posts and classify users?

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