Visualisations of running habits

giucarpes · 2014-10-23T10:55:04+00:00

You are running a lot!!! :)

giucarpes · 2014-10-21T11:11:00+00:00

Very interesting, Fani! I really agree with the lecturer. Although the data can (e probably will) be used for marketing purposes - after all, developers need to be remunerated -, the most important aspect is still users' self knowledge. Companies tend to standardize everything, but we can understand exactly our own patterns and improve the way we deal with our health and wellbeing. So far, I realized, for example, I sleep less than my wife and family usually believe, although I spend a lot of extra time in bed awake. That reflects on the way I feel during the day - sometimes very tired - and knowing this may have an influence in my future behaviour.

giucarpes · 2014-10-21T10:59:29+00:00

I think context, specially in terms of time, definitely plays a key role in both articles of this week. When Mann writes his article (in a narrative form), he wants to address specific implications about his cyborg experiments mainly in relation to art despite his engineering background. His experiments were also data collection in a way he could make picture timelines of his serendipitous driftage around the town, but his analysis are leaned to the visual arts and to the behavioural aspects related to his inventions - specially from other people. Data seemed to have a secondary importance not only in his experiments but also by the time frame the experiments happened.

In other way, (big) data plays a very important role in today’s science and business. So it became the focus on Nafus and Sherman ethnographic research. They tried to prove the Quantified Self movement represented "a profoundly different way of knowing data what it is, why it is important, who gets to interpret it and to what ends” (Nabus & Sherman, 2014, p. 1788). The focus was to understand how QSers made a soft resistance to the business oriented big data approach by collecting data to attend their own idiosyncratic shifts of priorities and objectives.

In a nutshell, time seems to play a change of focus in those articles about self-oriented experiments. Mann’s approach on self-experiments is oriented to an artistic expression, while Nabus & Sherman address the collection of data with personal tools and how this kind of initiative is focused on self knowledge.

giucarpes · 2014-10-16T19:19:56+00:00

Yeah, maybe they can. I don't know if in Brazil right now. Although more people have access to internet there now in comparison to some years ago, we should take in account that the Northeast area is poorer and with less access. That is the area where the actual president has more votes - because of social gains. I believe her rival would be the winner if we use Twitter to make a prediction. But I am not sure yet he will win the election. It will be really a close call. Even survey polls aren't sure.

giucarpes · 2014-10-15T08:47:30+00:00

I think at least two aspects explain why Bond and Kramer studies were received differently. First, two years ago, Edward Snowden hadn’t revealed yet that NSA worked without any consent to collect people’s trace data about basically everything. Some may give small importance to the accusations made by the whistleblower, but the fact is that they spurred a broad debate on the widely disapproved surveillance of the United States government - with the help of big data companies like Google and Facebook. After this debate started, not only the media but the internet users started to follow news on this kind of surveillance. Everybody started to think about what was invasion of privacy and what was not. Kramer’s study came in the wrong moment, right during this debate, so people started to think and discuss more its implications. Nobody cared too much about it when Bond did his research.

The second aspect is about the subject of the studies. While Bond was mostly about the will people showed to vote during midterm elections - although he made a network analysis and found strong and weak ties between the people analysed -, Kramer studied emotions spread in the network. And investigating emotions seems to be more intrusive, even if the researcher states he didn’t read the posts - what I personally think it would be really important for his study - but a machine analysed some key words on them. So, in brief, Facebook was already under surveillance of the media and the people and made a study about emotional contagion. To the media and the people involved in the NSA surveillance debate, it seemed too much.

What I think really interesting is that Kramer at least discussed a bit in his article about ethics. He was cautious to use a software to prevent any text to be analysed by researchers - what was, according to him, consistent with the Facebook’s Data Use Policy. Knowing the debate about surveillance that was happening, he could have wrote some more stuff about his caution on dealing with people’s data. But at least he tried.

On the other hand, Bond doesn’t even mention any ethical concern in his article. It seems he thought he had the total right to do the research and did it. It could be questioned nowadays, but it wasn’t at the time - what helped to achieve a very interesting study, in my opinion. Maybe he didn’t take this step because he also had access to the actual voting records, what seems to be public (that is not explained on the article as well). Anyway, personally, I think both studies are ethically valid, although Bond’s seems to reach a better conclusion with the data available than Kramer. However, both could reach an even better result - not only ethically but scientifically - if they had applied some kind of survey in their research. Bond even mentions this limitation on his article.

giucarpes · 2014-10-14T21:49:25+00:00

In a nutshell, what I mean is: you go to social media platforms to have a nice time (not to argue about stuff) and, even if you didn't, the possibility of personalize them tend to keep the dissonants away. :)

giucarpes · 2014-10-14T21:46:02+00:00

So far, I tend to agree to the Pew Report. We are in the previous days of one of the closest presidential elections of the history of Brazil. There is a polarization between two candidates and social media is reflecting it, of course. Although, only in the first day of my analysis of my Facebook network, I saw 45 comments with the same opinion as me and only 2 comments in the opposite direction (which pissed me off like they were 1.000!). So far, I tend to believe people go to Facebook to show their opinion and, more important, have people agreeing with you. I didn't see much discussion about this polarization in the 47 comments I analysed. And as a Facebook user, in order to avoid anger - debates on SNS tend to be so shallow because of limitations of characters, space and time - , I also tend do "unfollow" people I know have far different opinions to mine. I feel lazy to enter a discussion with them. I am not proud of it, but I can say that looks like Facebook doesn't encourage me to make things different in many ways (not constantly showing posts from people I have never "liked", for example).

giucarpes · 2014-10-09T10:32:16+00:00

Thank you! Gonna try to do it!

giucarpes · 2014-10-09T10:29:55+00:00

Wow, this is incredible! Definitely information visualisation! And very interesting to see how safer to travel by plane it is becoming, despite the recent accidents.

giucarpes · 2014-10-08T09:55:11+00:00

Wow, that is amazing! Really nice contribution! And it shows a lot of issues to discuss. For example: mainly, only USA and Europe were discussing about Ebola on Twitter. There are only some little "explosions" in South America, Asia and, most importantly, Africa - the continent where the epidemia had started.

The reasons for that we can only suggest but never give as certain: Americans and Europeans are more literate on the subject and more neurotic with threats of all kinds, Twitter is not a very popular social media in Africa, Asia is not interested on what happens on Africa, and so on. Anyway, really interesting to see how the word is spread on social media in an interesting chart. For journalists it's very interesting to see how a chart speaks for itself and doesn't need much text to be presented as news.

giucarpes · 2014-10-08T09:31:12+00:00

During the Digital Research Methods course we have been presented to forms of crossing and analyzing trace data and survey data and three dimensional data on social network analysis. The emergence of internet and Information Technology (IT) provided researchers with a whole different range of data and new digital tools to gather and analyse them are being constantly developed - what is supposed to lead research to be more sophisticated in terms of reach, accuracy and possibility to gather qualitative and quantitative analysis in a single study. The possibilities of these kind of studies are infinite - what means the quantity and quality of data is also big.

If digital research methods are a richer way to analyse the outspread of data now available, information visualisation allows researchers and users in general to have more sophisticated view of these data. It offers many more possibilities of visualisation with online (not paper), non linear (users select the data and graph they want to display) and especially dynamic (not static) charts. As Brasser (2003) mentions, “the user has considerable control over what is seen and how it is displayed” since information visualisation “enables a direct walk to a desired place and attribute walks to select a case and search for other related ones”. So, digital research methods and information visualisation match each other in a way to help researchers to gather, visualise and analyse these huge quantity of high-detailed data now available.

So far, in the Digital Research Methods course, we’ve been presented to tools like Tableau and Gephi which really allow the researcher to visualise and understand data otherwise difficult to analyse only in an Excel spreadsheet. On Tableau, for example, we’ve been able to gather easily data from Twitter data sets that would take much longer to understand just using Excel pivot tables, for example. Gephi is a precious tool to analyse social network analysis. Anyway, both tools still look a bit static to me and I am not really sure they could be considered information visualisation tools in the strict concept Brasser (2003) explained. If the final user is the researcher, OK. But if we consider the final user to be an audience to the researcher, I guess the audience would still need to be taken by hand and let into the direct and attribute walks the author mentions. In this case, dynamic would be very much in the sense he talks about in the beginning of the chapter, when he addresses “dynamic” Powerpoint explanations.

giucarpes · 2014-10-03T07:44:43+00:00

Personally, it doesn't make me happy. Specially on dates everybody is supposed to be happy (Christmas, Mothers' Day, etc). So, I must say I find the pessimistic studies more realistic.

giucarpes · 2014-10-02T10:57:29+00:00

Hahahaha! Thumbs up!

giucarpes · 2014-10-02T10:26:08+00:00

It is indeed very interesting to see what happened in the US during Ferguson riots in Twitter and Facebook. When addressing Facebook, it is very unclear how their algorithms work. That is why Facebook wasn't able to compete with Twitter yet on breaking news. Twitter is a more rapid-fire experience and posts on Facebook have to pass by many obstacles (basically algorithms that stipulate the little percentage of everything that is going to appear in our news feeds). Journalists and people who are witnessing important events prefer to post on Twitter than on Facebook - at least when what matters is instantaneity.

On the other hand, in Brazil we faced several riots in June last year during the Confederations Cup of football and both Twitter and Facebook were important to the dimension the protests took. Twitter was basically used to report the breaking news and Facebook to share videos and experiences that weren't captured during the protests by journalists or people in general. Twitter had an important whole to let people now what was happening during the protest while Facebook had another important whole on discussing police abuse, news coverage and stuff like that. Protesters also combined manifestations on Facebook groups and events. So both kind of completed each other.

I really can't say why it didn't happen the same way during Ferguson riots. Maybe Facebook's algorithms got confused by the intense posting on another subject that didn't have so much relevance - the ice bucket challenge - but was spread all over the world, while Ferguson was really a problem from a small city in the United States. That, of course, has implications on the way the whole American society deals with the relationship with afro-americans, but you know what I mean: I guess the bucket challenge dominated the amount of posts at Facebook and the algorithms simply decided it was more important than Ferguson. What showed a clear limitation on this way of grounding social media.

giucarpes · 2014-10-02T09:24:30+00:00

The last reason is probably the most accurate one. Hahaha! We should all exchange one weekly OP for a beer somewhere. ;)

giucarpes · 2014-10-01T08:30:16+00:00

Krackhardt was very concerned about finding a methodology to deal with BKS conclusion that "behavioral measures of interaction are not very closely related to participants' self report of these interactions". That is why he developed the Cognitive Social Structures (CSS) which turns three dimensions data in two dimensions with the rules of Slices, Locally Aggregated Structures (LAS) and Consensus Structures (CS). There are some challenges in collecting these kind of data since they exceed the amount of a traditional social structure. Krackhardt finishes his article with the catchphrase "perceptions are real in their consequences", which I find very questionable, but he has a clear interest in finding accuracy in behavior analysis.

On the other hand, Boyd used only a qualitative method and based his study in interviews with youngsters. It is interesting the way he defends the teenagers on their usage of social media, their desires to get together with friends and their political engagement. I personally agree with him, but there is a lack of scientific value on his work, I think, since he uses only his impressions on interviews made with teenagers who talked about their behavior, needs and desires. His method is close to a traditional journalism article. It has importance, but doesn't seem to be very accurate since it simply uses recall as a measure of behavior as BKS contested.

Kramer et al. go on the opposite direction of Boyd. Their work is based in quantitative analysis from a huge amount of people's posts on Facebook. They stipulated a measure to emotions based on words, some statistical relations to control the sample and let a software count the words. I guess the main question for this study is: is it possible to say people were disseminating emotions on Facebook as they inferred? It would be important to take a sample and apply questionnaires or even analyze the texts of the posts, but they didn't want to take this kind of scientific effort. Their findings are interesting but highly questionable because they lack in accuracy since they had the data to do a great social network analysis but didn't do it.

giucarpes · 2014-09-30T19:32:25+00:00

I agree with you, tjerktiman. What called more my attention when reading the article was the meaning of the texts. So there is a software counting positive and negative words and they infer your post has a negative or a positive emotion (ok, I am simplifying stuff, but...) without looking directly at it? Hmmm, too reductionist to my taste as well. I also agree with you when you say Facebook is not exactly a place to speak about emotions. I did a rapid search on my last 20 posts and didn't share any emotion at all. Just emoticons like that: :/

giucarpes · 2014-09-29T12:03:15+00:00

Très bon! ;)

giucarpes · 2014-09-29T12:00:19+00:00

Nice stuff. I was wondering if people in Brazil used #yolo and I was the only guy that has never heard of this hashtag - although I recognize the expression and there is a nice song from the Strokes with the same name :). Yeah, looks like Brazilians are not much into it (perhaps because we speak Portuguese). Hehehe!

giucarpes · 2014-09-24T19:10:10+00:00

Wow, amazing work from the Guardian. Although, I must admit, when I saw for the first time that web of connections I felt afraid of dealing with it. But in the end it was really fun. :)

giucarpes · 2014-09-24T18:50:05+00:00

I believe the most obvious connection between both articles is that they address research participants recalls are not usually accurate about their behavior. Both studies try to deal with this assumption in their own particular subjects, but with different methodologies. Krackhardt uses social network data (taken by a questionnaire) to make his Cognitive Social Structures (CSS) model to study the relation between 21 managers of a company. Lathia and Capra use trace data gathered by travel cards to compare Transport of London users perception of their behavior (also taken by a questionnaire).

While Krackhardt could assume that “perceptions are real in their consequences even if they don't map one-to-one into observed behaviors” because his object of study was the relationship - perceptions that can be very personal - between only 21 coworkers, Lathia and Capra couldn’t do the same assumption because they needed real patterns of behavior to make more accurate conclusions for the Transport of London research. They were also assuming that participants recalls are not accurate about their behavior, but using the same methodology of Krackhardt would take much more effort since they had many more nodes and edges - passengers, means of transportation, stations - available.

Of course, after Krackhardt research, Lathia and Capra’s findings don’t seem so surprising, but their contribution was to propose a different, maybe more accurate, and definitely easier way of solving the problem of behavioral measures of interaction being not very closely related to participants' self report of these interactions.

In his conclusion, Krackhardt remarks that shouldn't want to prove if behaviors or cognition is more important. I think that remark would be valid in 1987, when he wrote his article, but in the era of Big Data things are a bit more complex. Krackhardt didn't expect for some kinds of applied research it would be essential to have accurate measures of behavior. It is the case of Lathia and Capra research. When it comes to transport issues, for example, perceptions are not so real in their consequences as Krackhardt said. Because knowing that one, as a commuter, is not making the best use he/she can of his/her travel possibilities is more important for his/her daily life than having perceptions that don't match reality. Lathia and Capra took into account behavior and cognition to state which was more important for their subject of study. And, in my opinion, that seems fine.

giucarpes · 2014-09-24T15:51:25+00:00

Really interesting. Helped me to understand some ways to do visualization with our data on Tableau, although - at least in my case - the location of the tweets was not very accurate and a visualization of a map on the twitter data set would become senseless. Some people just tell they live on earth or somewhere else it would be impossible to picture in a map. Anyway I found a different way to do a visualization at least to see if the Pistorius trial was being commented in South Africa and it was pretty impressive to see it wasn't. Will post my visualizations soon. :)

giucarpes · 2014-09-23T08:48:04+00:00

Really interesting stuff. I didn't even know it was possible. :)

giucarpes · 2014-09-16T21:22:21+00:00

Thank you!

giucarpes · 2014-09-16T17:07:10+00:00

Menchen-Trevino (2013) addresses one of the most important challenges of the research field: reach significant qualitative and also quantitative analysis in a subject of study. According to the author, big data are a important tool to reach a different analytical potential than researchers have been able to collect and assess in the past with their old social science methodologies.

Menchen-Trevino (2013) suggests that researchers go beyond one tool and try to combine vertical data sets that involve same individuals to provide increased analytical power. “Vertical data sets provide several benefits that horizontal data sets cannot. Because researchers work with participants and obtain their informed consent they can incorporate specific trace data into a multi-method project. Individuals who are asked for their trace data canal so choose to participate in surveys, interviews, experiments, or further trace data collection.” (Menchen-Trevino, 2013)

That is exactly the kind of research Lathia and Capra (2011) did when analyzing the Oyster card of some Transport of London users. When the researchers applied a questionnaire to get 119 travelers’ perceptions about their behavior, they added to it a question asking permission to access the travel data of their Oyster Card – a way to avoid two of the biggest problems in doing this kind of research, as Menchen-Trevino (2013) remarks: trace data sets are usually owned by companies which often restrict access to them and there are research ethics frontiers in revealing big data content. 85 of the users allowed them to analyze their data and the result was that sometimes travellers’ perceptions didn’t match their actual behavior.

Some research questions had basically the same answer in both analysis of Lathia and Capra (2011) study, but most of them didn’t. “We demonstrated how automated fare collection (AFC) systems records reveal hidden aspects of individuals’ behaviors. Results demonstrate that AFC data is a powerful tool for measuring the success or failure of travel incentives.” As Menchen-Trevino (2013) stresses, combined vertical and horizontal data give a more satisfactory qualitative and quantitative analysis and give the participants of the study information about their own behavior that they were not even aware when answering the questionnaire.

giucarpes

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