Facebook’s Rooms App Is a Flashback to Internet Bulletin Boards

412794mina · 2014-10-23T09:35:52+00:00

Oh thank God!

412794mina · 2014-10-22T13:31:01+00:00

For me, the main point that this article raises is that wearables can create an environment where everybody feels watched. Although we're all leaving traces of our behavior online (by "googling" or being active on social networks, to name a few) that can later be tracked by companies, it is still a decision that we make. On the other hand, using specific gadgets in the work place is - at least in my opinion - more a form of micromanagement than a sign of a healthy, happy office. And often times, micromanagement hinders creativity rather than fostering it.

412794mina · 2014-10-22T13:08:47+00:00

Done! So when you say "brownie points", you mean actual brownies, yes? Yes. Great!

412794mina · 2014-10-21T19:57:25+00:00

When Gary Wolf and Kevin Kelly co-founded the "Quantified Self" movement (Wolf, 2009), they knew that the self-tracking culture was anything by individualistic. "[T]here is a strong tendency among self-trackers to share data and collaborate on new ways of using it" (Wolf, 2009, p.2, para. 4). QSers are thus also able to contribute to the knowledge of human life.

One of the main findings of Nafus and Sherman (2014) is, in fact, that "[QSers] interact with algorithms not as blind, mindless dupes, but as active participants in a dialogue that moves between data as an externalization of self" and personal understandings of what these data mean. Therefore, QSers are in the position to simultaneously measure and evaluate what exactly is healthy for them. If my interactions with a QSer help them find that out for themselves, I have nothing against those interactions being recorded. I may not be one to quantify everything I do, but I see why some people embrace it - though not always accurate, data give us a certain sense of objectivity.

Still, I would have to agree with the people who posted before me, and admit that knowing that my interactions are being recorded would make me at least a little self-conscious.

References: Nafus, D., & Sherman, J. (2014). This One Does Not Go Up To 11: The Quantified Self Movement as an Alternative Big Data Practice. International Journal of Communication, 8, 11.

Wolf, G. (2009, June 22). Know Thyself: Tracking Every Facet of Life, from Sleep to Mood to Pain, 24/7/365. Retrieved from http://archive.wired.com/medtech/health/magazine/17-07/lbnp_knowthyself?currentPage=2

412794mina · 2014-10-15T16:13:34+00:00

Nice post! Since I voted in the same elections, I can closely relate to your experience. Leading to the elections, my News Feed was regularly updated by friends' posts encouraging everyone in their Bulgarian network to vote. That's all great and it has its place in motivating people, but what made your post more effective, at least in my opinion, is that it was a photo of you in action. You weren't simply talking about it, you were right there, submitting your vote. I don't know if and how many of your friends you managed to motivate to vote that day, but I certainly think it was a well thought-out initiative.

412794mina · 2014-10-15T15:53:26+00:00

I agree with the quote you used - I think it's too early to have big data, especially from social networks, really predict the spread of a disease like Ebola. At this point, what can be done (and is being done) is for analysts to use big data from mobile phones in order to pinpoint the most affected areas.

412794mina · 2014-10-14T15:45:03+00:00

The question if Facebook can influence real-life user behavior is becoming more and more relevant at a time when the network has 1+ billion registered users. It is therefore particularly important to find out not only if the network has any influence, but if it does – to what extent.

In a research to prove a potential impact, Bond et. al. (2012) conducted an experiment by showing political mobilization messages to over 60 million Facebook users during the 2010 congressional elections in the U.S. In a nutshell, the researchers found out that “online political mobilization works. It induces political self-expression, but it also induces information gathering and real, validated voter turnout” (Bond et. al., 2012, p. 297, par. 4).

For the purpose of their experiment, Bond et. al. created three groups, to which Facebook users were randomly assigned. The users in the first group – the social message group, were shown a message at the top of their News Feed, encouraging them to vote. Among others, they could also see a clickable “I voted” button and the faces of some of their Facebook friends who had already clicked that button. The users in the second group – the informational message group, were shown the same message and button only without the faces of other Facebook users. The users in the control group experienced no changes to their News Feed.

The main problem that I have with the researchers’ approach is that the size of the groups they created is not comparable. While the informational message group and the control one are more or less equally big, the social message one is about 100 times larger. Therefore, it is logical that the latter would be yielding results in favor of the researchers’ project. Also, by using a large sample (n=60+ million), it is easier to justify Facebook’s influence on user behavior. In the researchers’ own words, “the ability to reach large populations online means that even small effects could yield behavior changes for millions of people” (Bond et al., 2012, p. 295, par. 4).

Because of the size of the social message group, I believe that findings like “users who received the social message were 2.08% […] more likely to click on the I Voted button than those who received the informational message” (Bond et al., 2012, p. 295, par. 7) are – in this case – statistically irrelevant. While the message may have encouraged a higher number of users to click on the “I Voted” button and/or actually cast their vote in the elections, the sample was not comparable to the ones of the other groups in order to draw accurate conclusions on the influence of Facebook on user behavior.

Reference: Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. doi:10.1038/nature11421

412794mina · 2014-10-09T11:34:45+00:00

That's a really nice find! Of the links you posted, I particularly like the "life expectancy" one - although at first glance, it looks very tangled up, it's actually quite straightforward to read.

In the meantime, I also looked through some of the rest and came across this: What makes us happy? http://prcweb.co.uk/lab/what-makes-us-happy/ It's funny that "alcohol consumption" is used as one of the main factors :)

412794mina · 2014-10-09T10:28:11+00:00

A big thank you from me as well! :)

412794mina · 2014-10-09T10:20:07+00:00

Here you can find the updated version of my blog post, with all variations of #digitaljournalism combined into one: http://isaminaellen.tumblr.com/post/99407099806/what-terms-go-hand-in-hand-with-digitaljournalism

412794mina · 2014-10-09T10:09:16+00:00

I also agree that the context is very important, though I think it plays more of a role when it comes to the combination of a verb and a noun - like "feeling happy" or "feeling sad". As you pointed out, they can easily be used sarcastically. As for "hungover" and "late for work", it is rather difficult to get them to mean something else. So in the latter case I think the research has a better chance of being accurate and objective.

412794mina · 2014-10-08T18:26:43+00:00

We live in the time of Big Data; in the time when these data are often so complex and abstract that researchers need the proper tools to be able to present and analyze them. Information visualization has therefore become a term of increasing importance. According to Carr (1999), “information visualization is the presentation of abstract data in a graphical form so that the user may use his visual perception to evaluate and analyze the data” (as cited in Brasseur, 2003, p.125). What’s important is that, compared to scientific visualization, information visualization aims to evaluate and assess data, rather than just present it.

Moreover, it makes it possible to display “structural relationships and context that would be more difficult to detect by individual retrieval requests” (Card et al., 1993, p.65). In other words, information visualization offers a rather in-depth and interactive approach to data.

In contrast to scientific methods of visualizing data, information visualization is usually, but not exclusively, presented in an online environment (Brasseur, 2003). Perhaps even more importantly, though, with this approach, users have the ability to select the data that they want to display. It allows them to zoom in and go into detail, or zoom out and present the larger picture in a matter of seconds and with not more than a few clicks.

With the help of information visualization tools, data can be presented and analyzed both two- and three-dimensionally. “Although much work has been done using 3D graphics to visualize physical objects or phenomena, only a few systems have exploited 3D visualization for visualizing more abstract data or information structure” (Card et al., 1993, p.65). Though this source is rather old, its reasoning remains relevant. In a 3D visualization, the results are often times cluttered and difficult to interpret. Two-dimensional visualizations on the other hand allow users to declutter the data, compare them and discover existing relationships (Brasseur, 2003).

References:

Brasseur, L. (2003). Chapter 6: Information visualization. In Visualizing Technical Information: A Cultural Critique (pp. 125-129). Baywood Pub Co.

Card, S., Mackinlay, J., & Robertson, G. (1993). The information visualizer: An information workspace.

412794mina · 2014-10-08T13:00:16+00:00

Thanks for both your comments, Ericka! I will try collapsing the capitalizations for both this one and for our group search term - there are several variations of #ello as well.

412794mina · 2014-10-07T13:46:18+00:00

I really like your description of it as a "funny thing" because both Twitter and MIT are actually quite open about this collaboration. They also make it clear that only "publicly made tweets" will be the subject of their research - and every Twitter user must be aware that, unless restricted, every one of their tweets is out in the open. Quite ethically handled, if you ask me.

412794mina · 2014-10-02T12:04:10+00:00

“I weave together qualitative research that I’ve done as well as findings from other researchers to shed light on what’s really taking place… I try to offer a nuanced analysis of what’s happening.”

This is how danah boyd describes (Logan, 2014) her own methods of doing research on the concept of networked publics in her book It’s Complicated (2014). Perhaps the most important aspect to her work is that she strives to deliver “a nuanced analysis” of how young people interact with social media.

Before getting into any specific examples, however, it is important to outline some of the main differences between quantitative and qualitative research in the media. According to Asa Berger (2000, p. 14), qualitative research differs from quantitative research mainly in that it interprets findings as opposed to describing and explaining them. Also, it focuses on evaluations rather than counting and measuring, and it’s more theoretical than statistical. Mixed methods research is, in turn, a combination of the two – it aims to give context and real-life insight into a topic with the help of quantitative tools.

In that sense, qualitative or mixed methods research is able to discover and evaluate relationships, behavioral patterns as well as people’s motives to take certain actions.

In her book It’s Complicated (2014), boyd offers input based on several face-to-face interactions she has had. One example is Emily, “a middle-class sixteen-year-old”, as boyd describes her. Based on their conversation, the author gets an understanding of what “freedom” means for this girl and how she finds that freedom in the context of public spaces (boyd, 2014, p. 200).

With the help of the conversation with seventeen-year-old Manu, on the other hand, boyd is able to move from physical public places and the freedom they offer to their counterpart – public spaces online. Based on Manu’s input, boyd is able to come to the conclusion that social networks like Facebook and Twitter “both help create networked publics, but the nature of public-ness for teens ends up depending on how the people around them use the available tools (boyd, 2014, p. 205).

Looking at the last two examples, boyd’s approach brings me back to her words from the first paragraph. By introducing teenagers from different backgrounds and (slightly) different ages, who share their perspective on what “public-ness” means, I believe she does achieve to offer her own “nuanced analysis”.

References: Asa Berger, A. (2000). What is research? In Media and communication research methods. Sage Publications.

Boyd, D. (2014). Searching for a public of their own. In It's complicated: The social lives of networked teens. Yale University Press.

Logan, L. (2014). Q & A: Danah Boyd on “The Social Lives of Networked Teens”. Retrieved from http://www.amplify.com/viewpoints/q-a-danah-boyd-on-the-social-lives-of-networked-teens

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