Summary:

In the paper “The Language that Gets People to Give: Phrases that Predict Success on Kickstarter,” the authors look into the power of phrases and words to encourage users to users to donate and use them as an indicator for the likelihood of a project being funded or not. They mainly focus on the language used in the project pitch but factor in other variables such as amount requested and timeline for the project. However even with the inclusion of other variables they found that the top 100 predictors were still phrases. The phrases were also classified into 13 overall categories: art, music, publishing, design, film and video, tech, dance, theater, photography, food, games, fashion, and comics. Each had respective sub-categories and the phrases were analyzed to see if they were exclusive to a single category since those would be useless and skew results. A lot of what the paper also found supported my experiences as well. Users love getting something in return and a lot of kickstarters will offer “limited editions” or “exclusive” items in return for reaching certain levels of donation. This is actually added to another grouping of categories: Reciprocity, scarcity, social proof, social identity, liking, and authority. All of which are widely used tactics in selling things to people

Reflections:

I will start by saying that throughout the paper I was wondering if I could take advantage of these phrases to try and get myself funded for something but then I realized that these are just indicators. I did take not of a lot of the analysis that went on in the paper, more so than previous ones. This one actually had very descriptive explanations of what each analysis was doing and how they worked. So not only was it informative from a research standpoint but I found it to be very useful from a student one as well. I’m not sure how much the words really do play a part though. I feel like personal interest is the biggest deciding factor and people are often willing to invest more in things that they like. So within six categories of persuasion was the information skewed towards one category over the others in statistically significant way? This article was still very interesting. We really do take everything for granted when the time and consideration people put into wordsmithing could easily be worth it.

Questions:

• Do these phrases hold true across all donation/crowdfunding sites?
• Do identical phrases using synonyms have the same impact?
• Inclusion of stretch goals and updates didn’t seem to be included but if they were to be would they have any appreciable impact?
• Do these phrases have any implications for advertising in real life? Could they affect in-person donation drives?
• Would this study be more useful if they were able to take into account all donations and how much each was for?

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Summary

In “Antisocial behavior in online discussion communitites,” the authors discuss the multitude of ways they tried to determine antisocial user accounts in such a way that the likelihood of a future ban could be predicted. The studies conducted focused on three sites with fairly large user bases; IGN, Breitbart, and CNN then divided users into two groups: Never-banned users and future-banned users. They then divided future-banned users into two more distinct categories of those who experienced high rates of post removals and those who had fewer removed over the course of their online life leading up to the ban. Their studies also indicated that everyone’s content quality slips as their life on a site increases, future-banned users overall had generally lower content quality to begin with.

Reflections

I found this article to be very interesting, but am not quite sure if this has any implications on prevention of trolling online. They didn’t go too much into how they determine which comments are considered to be instigator comments, but I imagine it would be difficult to differentiate between responses as more often than not the responder is more aggressive than the creator. Some users create accounts with the expressed purpose of being a troll account so their analysis that deviant users exert more effort into singular threads or conversations makes a lot of sense. Since they are trying to instigate and inflame others. However, how would they differentiate between actual trolls and users who are simply voicing their unpopular opinion. I think that this work is interesting from an analytical standpoint but in practice this could create a white-washed environment where unpopular or dissenting opinions could be quashed before a person even has a chance to defend themselves.

Questions

 

How, if at all, would they differentiate between users with unpopular opinions and genuine trolls.

Some antisocial users behavior degenerated due to response from the community. What could be done to stop the degeneration or backlash?

Censorship is a problem, but is it Ok to silence others who’s statements you perceive as inflammatory?

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Summary

In “Social Translucence: An Approach to Designing Systems that Support Social Processes,” the author discusses the importance of the visibility of social information in recreating certain aspects of physical interaction in the digital realm. Awareness and accountability of our surroundings and actions largely govern how we act in person but online communication spaces may be devoid of all that. The author proposes three approaches to answering the question on how to bring social cues to a digital space: realist, mimetic, and abstract approaches. They attempted to design a infrastructure to assist in small group settings that could provide textual and graphical representations of users social information called Babble.

In “The Chat Circles Series: Explorations in designing abstract graphical communication interfaces. Talks about how textual communications have evolved to be more interactive and what interactivity and features mean for social interactions on digital platforms. They created a text chat that introduced proximity based chat via visual representation, just like in real life where you generally must be nearby to participate in a conversation. They also included visual representations of a users past presence which allows other users to identify hotspots of interaction. They even discussed many of their other interface designs used like TeleDirection, Chat Circles 1 + 2 to engage users and better convey emotions not present in purely text conversations.

Reflections

I had never really considered how much visibility and awareness play into talking to others effectively. It would make sense since a large portion of how we react to given instances or conversations is based off of reading body language and the speakers intonation. However the thought never crossed my mind how it impacted digital interactions. The ability to make an online discussion readable, not just to those participating, is a huge part of making sure information or opinions are disseminated correctly. If you look at sites like reddit, user awareness is promoted via the voting system so others are able to attach and incorporate new trends into their own subreddit comments.

I could draw a lot of parallels between what they were trying to do in “Chat Circles” with technology now. For example their TeleDirection idea is similar to twitch streamers who stream their daily lives and accept input from people watching or following the stream (although the space isn’t so much collaborative as it is caustic). Some of the other interfaces they included such as being able to label other users (tagging like on reddit) but the graphical representation of other users via their reputation could easily be abused. It made me think about most of the features I now see incorporated into my frequently used messaging/chat systems and a lot of what was pioneered in this piece evolved in one way or another into fairly common implementations. It’s interesting to think about how there’s so much that can be done with current technology to interact and shape other users online appearance besides simply “replying” to them.

Questions

How would the task of bring social cues to the online world be improved via current technology.

For the author of “Social Translucence” why does he consider so many technologies to be walls between people rather than their more apparent use as bridges?

Hindsight is 20/20 but what sort of estimation can be done on a users future behavior?

Is expressing emotion to strangers via text really necessary?

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Summary

In “Identity and Deception in the Virtual Community,” the article discusses how disjoint we are from our actual personalities in an “anonymous” space. Specifically online where others may latch on to other defining features of your online persona in order to identify you or how they expect you to act (i.e. a goofy avatar, your writing style). Usenet was this article’s example of choice. It then took on a signalers and recievers model to explain this honesty and deception similar to how biologists and game theorists will analyze interplay between truthfulness and deceit in a communication system.  Assessment signals, were ones that followed Zahavi’s Handicap principle, where a user must possess a trait to send the signal. However those that do not follow the handicap principle are called conventional signals. These are the ones where it is open to deception and manipulation.  In most cases conventional signals would cost less to send than assessment and can easily be used by trolls and catfishers in an online environment.

In “4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community” 4chan, an online messaging board, was examined. More specifically /b/, one of the more notorious sections of the site. The article then examines what sort of role the anonymity and ephemeral nature of the posts plays in what users are willing to create and distribute on the site. The anonymity has also cultivated a culture within users of the site, although the culture it has created is not exactly good by any measure the anonymity has created camaraderie amongst the anons.

Reflections

The article “Identity and Deception in the Virtual Community” really highlighted that online we can be whoever we want to be. We can claim expertise in areas where we have none. We can fabricate experiences that we’ve never had. In fact some people will even create multiple accounts, like troll or smurf accounts, online to cater to their other preferences. It can provide leeway for users to do and say things they’d never do in real life but also create a safe buffer for those who are less outgoing or more concerned to join in discussions freely.

The fast pace nature of 4chan is pretty well known and in most cases not for a good thing. I found it interesting that ephemerality was studied as it didn’t take into account the fact that many threads are archived via users. The distribution of content through anonymity was also interesting to see as requests and discussions, which I perceived as being the majority of the content, were much smaller in terms of presence.

Questions

What drives others to create malicious personas?

Is deception always for the worst in online environments or is that all that we hear about?

Why is it important to maintain ephemerality in an online to some?

Anonymity online is often very shallow so why does it give others so much confidence?

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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 1 Sept. 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 1 Sept. 2017.

 

Summary

 

In “Is it really about me?: message content in social awareness streams,” the researchers sought to study how users treated twitter with respect to the kind of content they would post. What they ended up doing was classifying the content of posts and using that to determine what sort of user someone actually was, either a meformer(majority posts are about themselves) or an informer(majority posts provided non-user information).

 

While in “Why We Twitter: Understanding Microblogging Usage and Communities,” it was more focused on how users interact and how communities are created between those users given the rise of microblogs. They were able to check geographical distributions and user intentions before designing three groups that they believe encompass users: information sources, friends, and information seekers.

 

Reflection

 

I find it impressive the deductions made from data grabbed by third parties and the analysis and can only imagine what sort of nefarious metrics twitter themselves is probably conducting. It was interesting to see how they classified users in the above papers, but I can’t help but feel the catagories are too broad. In “Is it really about me?…” I felt that their sample size was to small and couldn’t account for possible biases so it was a little difficult to take to much stock from theirs.

 

Questions

 

– Does twitter artifically shelter sub-communities?

– Why is it that twitter succeeded where others failed?

– What makes twitter a preferred platform. I see the shortened versions of things to be very misleading?

– What other metrics might be more useful to have if twitter is not already collecting them?

– How accurately can major events be predicted based off of social media (i.e. arab spring)

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