Summary

In the paper “Visualizing Email Content: Portraying Relationships from Conversational Histories”, the authors document the ideation, algorithm design, and content analysis of their application Themail. The purpose of this program is to give users a photo-album type visualization of their relationship with another person using the user’s archived e-mails with that person. Themail takes the most frequently used words exchanged by e-mail between the pair, checks its uniqueness (whether it also frequently exchanged with many other people), and uses an algorithm to arrange the words in one of two meaningful formats.

• Needle Mode: In the foreground, vertical stacks of words are placed on a timeline by month. Each stack consists of the most frequent/unique words exchanged during that specific month. This provided a more detail-oriented exploration of one’s e-mail archives. Despite its capability for searching these details, only 20% of users to greater interest in this mode.
• Haystack Mode: In the background, larger words float faintly but visibly. These words represent the most frequent/unique words exchanged over the course the year being represented. This provides a more “big picture” exploration of trends and themes. 80% of users utilized this mode the most, indicating an interest in the greater relationship they had with people and the aesthetic quality of the application.

The algorithms used to generate word value and populate the screen was based off of a past algorithm which scores words based on their relative frequency in one document out of a collection. The team evolved this concept by comparing subsets of e-mails against supersets so that they may not only take relative frequency into account but also relative uniqueness. The equations for yearly and monthly word values are nearly identical, with the key difference being that yearly word frequencies are cubed in order to increase overall weight results.

 

Reflection

There were two aspects of this paper that interested me. Firstly, the aesthetic factor that made users more interested in the haystack rather than the needle mode goes to represent how important it is to remember that despite the technical nature of our field, a appreciated level of graphic design is involved in computer science. Secondly, the algorithm design brought to mind my team’s project for the semester. We too must come up with an algorithm that utilizes word frequency to detect importance in a certain context. While our topic is not based upon time, the paper nonetheless provides us with a unique perspective and possibly a stronger foundation from which we can build our equation. Namely, the use of inverse frequency to detect relative frequency rather than raw frequency may allow us to see if we’re focusing on the wrong keywords (as we are currently measuring by the latter). 

 

Questions

• What other visual structures and organizational patterns were considered for this application? What benefits did this horizontal timeline with vertical stacks afford over the other options? What drawbacks did you have to accept with it?
• The paper mentions the limitations of their content analysis in that the application cannot detect personal weight of e-mails. For example, an e-mail from a mother wishing happy birthday to her son might be taken as having greater word value than one where she is reminding him of a dentist appointment. Is there any progress being made on that front? What ideas are there for identifying overall message value?
• What factors went into deciding to have the haystack and needle mode occupy the same screen rather than having a way to switch between the individual modes? Can you see any benefit to having a screen for each mode rather than a fused screen? Perhaps it would allow more features?

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Summary

The study ‘The Language that Gets People to Give: Phrases that Predict Success on Kickstarter” seeks to understand the effects social communication can have on a kickstarter’s ability to pull in donations at faster rates. The study utilized scraping software to analyze over 45,000 crowdfunded projects from Kickstarter.com, over 9,000,000 phrases, and a multitude of other variables. Some key factors that the study concluded as correlating to increased donations include:

• Reciprocity: Patrons are more likely to respond to a project if they believe that they will receive something in return for their donation.
• Scarcity: People place higher value on products they believe to be limited in quantity or exclusive to a certain group of people (typically those who donate above a threshold).
• Social Proof: Users are more likely to follow in the footsteps of previous donors who received recognition and/or praise for their participation in the crowdfunding.
• Social Identity: People respond to a sense of belonging, especially if it is to a group they already identify with. In order to further perpetuate their sense of belonging, they will donate to validate their presence within the community.
• Liking: Fairly straightforward, a product or a person behind a project will achieve greater success if it/they are well-liked.
• Authority: Greater attention will be given to a project where an authority figure is present (i.e. a film based project involving a fairly well-know or well-respected director).
• Sentiment: People are more likely to respond to a project that provides them with a deeper emotional reaction (positive or negative).

 

Reflection

            It makes sense that the above listed factors should influence people’s decision making process online since they are fairly reliable ways of influencing people in the physical world. The concepts of reciprocity and scarcity are seen all the time in marketing and commercials for products (“buy within the next 10 minutes and get a second one free”). Social proof, social identity, and liking is apparent when products point out how past users had positive experiences. Authority can be seen in celebrity endorsements such as having famous actresses in make-up commercials. Sentiment is apparent in television based donation-seeking commercials such as the American Society for the Prevention of Cruelty to Animal’s (in)famous one involving images of sad animals displayed over the song ‘Arms of an Angel’. Even the negative phrases have a logical point, such as the phrase “even a dollar” allowing users to get away with giving sparse amounts of money rather than encouraging large donations through more positive reinforcement. The data mining techniques the study used were also very interesting as they give me a lot of ideas about how they can be applied to our semester project. Seeing a study where R was utilized to a more advanced degree than we observed in class was also a distinguishing tie-in.

 

Questions

• How greatly can phrases with equivalent meaning but different wording impact success? For example, two phrases that offer the same level of reciprocity but with different phrasing.
• How much does pop culture benefit/hinder the area of ‘social identity’? How much can too much specialization into a certain group of people hurt a kickstarter’s ability to attract donations or attention?
• Can this research be applied to sites not involved with crowdfunding (such as a regular social media site or a blog’s ability to attract fans)?
• Where can we see crowdfunding marketing evolving given this data?
• People often read the comments of a kickstarter to get a feel for it? How can the presence of comments be added to this study to provide greater range of perspective?

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Summary

The article “Antisocial Behavior in Online Discussion Communities” describes a study that sought to characterize the behavior and evolution of antisocial web community users over time. The study focused on users from three websites: CNN, Breitbart, and IGN. Users under observation were split into three categories:

• FBU (high): Users who were eventually banned and had many of their posts deleted.
• FBU (low): Users who were eventually banned and had fewer posts deleted.
• NBU: Users who were never banned.

In order to aid their characterization, the authors observed the frequency with which these users posted, the readability of their posts, and the disdain with which the community backlashed. A point of focus was how users in the FBU category had lower quality content from the start of their membership in the community compared to NBUs. FBU content quality and readability saw a noticeable downward trend as time goes on, and these users tended to post more frequently in fewer discussion boards. This type of activity received a greater backlash from the community and eventually led to their banning. A method used to potentially identify antisocial users early on was derived from the data. The authors discovered that users who received a large amount of backlash, many deleted posts, and more quickly deleted posts early on in their membership were extremely likely candidates for banning later on.

Reflection

Overall, the study was one that anyone from my generation would be able to relate with. Observing similar trolling and flaming in my day to day online life, I found it interesting to see how these users can be characterized. This article is helpful to me and my project partner since our current project idea involves a method to use automated moderation and identify/delete posts that veer from the discussion topic. While the article focuses mainly on negatively perceived posts and comments it was intriguing to see how some trolling behavior was positively perceived by the community. I can imagine a right wing user making an unpopular opinion on a left based political site then being chastised by a leftist user. It is not hard to see the community egging on such behavior. It makes me wonder how moderators should truly respond to this situation. If the behavior goes unpunished, then a toxic community is formed where attacks on unpopular opinions are allowed. If the behavior is punished, the website may lose the faith of its users for “supporting” the unpopular opinion and not the opinion of the toxic user who is considered a true member of the community.

Questions

• Breitbart is considered to be a far-right news source. What observations can be made about leftist users who started out by stating an unaccepted opinion and gradually became more aggressive in their behavior?
• What observations can be made on those who witness antisocial behavior? Do they become more likely to engage in such behavior themselves? Do their responses to such behavior become more aggressive over time?
• It was mentioned that users who were unjustly banned returned with much more antisocial behavior. The study claims that certain activity patterns can be used to identify antisocial users early on. Do you think taking such pre-emptive action could lead to unjust banning and thus the creation of new trolls and flamers?

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Summary

The Chat Circles Series

This paper drafts the designs and observations of multiple stages in a text based communications program. The impetus behind this software was to take the now mundane and relatively emotionless activity of texting and provide it with some semblance of the life found in face to face interaction. Various methods of user representation and graphical motion are used to emulate the experience of taking part in conversation with a group of people. There are features that express distance between two people, emotional tensions, and the interactions of being in an environment with its own independent happenings (such as news that plays in the background and potentially stimulates conversation). From a minimal set of breathing circles, the software evolves into a chatroom as vibrant with stimulus and emotion as a real gathering.

Social Translucence

            This paper notes how the factors of visibility, awareness, and accountability drive certain aspects of interaction and real life. Furthermore, it explores how the absence of these factors affects virtual communication as well as what can be done to remedy this absence. These elements engage two interacting people with a set of rules that defines acceptable behavior. Without them, the gloves are off and truly genuine interaction becomes difficult. The paper provides three forms of solutions:

• Realist: Projecting social information from the physical into the digital domain.
• Mimetic: Represent social cues from the physical into the digital domain.
• Abstract: Portraying social information in ways not closely tied to their physical analog.

These solutions provide different modes of injecting virtual communication with the aforementioned factors.

 

Reflection

The Chat Circles Series

            I’ve always been intrigued by how some developers tackle the issue of breathing life into virtual communication. Both articles share a common ideal in this regard. The various chat circle programs seem to take a cue from the points mentioned in Social Transluence in that they seeks to represent real life social cues in the digital world through the continuous movement and manipulation of circles. I must also note that, while this goal is interesting, I question whether the general population desires it or not. I am under the impression that a lot of people appreciate the difference between real life and virtual communication. Different sets of rules afford them different abilities. For example, the proposed features of Chat Circles would allow people to see whether you’re listening to the conversation or not. However, popular chat domains like Facebook Messenger or GroupMe have no such indication. Many users appreciate Facebook’s notification that the other person ‘has seen’ your message because of the fact that one can read the message that popped up without clicking on it (thus sending the notification).

Social Translucence

I can certainly recognize how it’s not just the tone and body language of a person that affects communication but the physical environment the speakers are in.  Most social media websites are of a public nature. Facebook, Twitter, and Instagram’s primary feature involves posting something for all of your friends to see. This wholly affects what you’re willing to say, how you say it, and does not really allow for intimate communication between two individuals without the use of a chat tool. It makes me wonder what a site would be like where a person has multiple friends and pages dedicated to each of them. Only the user and that specific friend could access and update that page, reminiscent of a private diary shared by two people. How would this affect their activities?

Questions

• Does the general population really want more intimate virtual chatting or do they have an appreciation for the emotive disconnect that comes with it?
• How efficiently can the models proposed in Chat Circles evoke certain powerful emotions? The article noted aggression or disdain but can such feelings be felt without true presence?
• How much extra effort do these modes of communication require with the addition of such features? If people do desire these features, how readily will they accept the extra effort?

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