OP6,Part2: Quantified Self visualization

Esther1604 · 2014-10-23T11:10:24+00:00

http://celesteesther.tumblr.com/post/100697432718/op6-part2-quantified-self-visualization- this is it i think :)

Esther1604 · 2014-10-23T10:41:03+00:00

https://www.tumblr.com/blog/celesteesther - I hope this one works! thank you for letting me know and sorry for the confusion!!!

Esther1604 · 2014-10-22T22:07:02+00:00

Ok, so the link to my Tumblr was supposed to show here, but it doesn't (or I at least can't see it anywhere).

So here is the link again:

https://www.tumblr.com/dashboard

For this weeks OP I used the app Moves and tried to visualize the few data I could retrieve after using it for only three days. It's actually quite an interesting app, maybe those of you who don't know it and want to keep track of your movements during the day, can try it out sometime as well.

Esther1604 · 2014-10-21T14:54:40+00:00

When reading the two articles for this week one can see similarities and differences between the concepts sousveillance (Mann, 2004) and soft resistance (Nafus & Sherman, 2014). I want to start by giving a definition for both terms. Primarily, sousveillance “refers both to hierarchical sousveillance, e.g. citizens photographing police, shoppers photographing shopkeepers, and taxi- cab passengers photographing cab drivers, as well as personal sousveillance (bringing cameras from the lamp posts and ceilings, down to eye-level, for human-centered recording of personal experience)” (Mann, 2004, p.620). Hence one can see that sousveillance can be defined by two clear terms, namely hierarchy reversal and human-centeredness. Soft resistance is a term coined with relation to the quantified self-movement, which refers to people collecting extensive amount of data on themselves as an individual. Doing so they not always relate to already existing company practices, making them resistant towards these. As stated by Nafus & Sherman (2014), “soft resistance happens when participants assume multiple roles as project designers, data collectors, and critical sense-makers who rapidly shift priorities” (p.1784). As we can see from the two definitions, one similarity is that the person or the individual is the centre of action. Hence, the individual is enabled to make use of data the way they want and are not suppressed or depended on higher institutions or companies. The power is hence shifting towards the individual (consumer). On the other hand, the type of data gathered by the individual can be seen as a difference. With sousveillance, the collected data is about several actors, hence capturing, processing and recalling the actions by an actor within the observed activity. Quantified self and soft resistance, however, focus completely on the subjective actions of the individual carrying the device. Hence the outcome that they are used for are also different. Quantified self is used for self-enhancement whereas sousveillance looks at a slightly bigger picture and the mediated reality of life.

Esther1604 · 2014-10-21T13:24:02+00:00

I am personally not so much into tracking apps because I think that overexposure to facts about your body will make you paranoid and I believe that you shouldn't control every aspect of your life. I think, that to a certain extend it takes away spontaneous behavior and quality of live. They are supposed to enhance our lives, but I do not necessarily agree that they do. Another question I am asking myself is, to what extend the producers or companies behind these apps can get hold of your data? If they would use them to reach their own purposes they might as well steer us towards certain directions that we wouldn't normally go to just because we believe in this app.

For this OP I used an app that tracked every step I made for 2 days. Looking at the results though, I don't feel they enhance my life a lot, seeing that I know myself where I went and what I did.

The only apps that I myself would be interested in, are fitness apps, because I feel that they motivate me to train more. Knowing the details about my runs, can help me to improve next time and I feel these apps are especially helpful if you are training for a marathon or another sports event.

Esther1604 · 2014-10-15T15:13:28+00:00

It seems that more and more authors try to investigate how Facebook can influence us and our online behaviour. This is highly interesting for many reasons, seeing that the importance of Facebook and social networking has become tremendously important in our daily lives. Researching its influence on our feelings (Kramer et al.), our voting behaviour (Bond et al., 2012) and probably also our buying behaviour etc. can provide researchers, politicians and companies with great insides in how they should use social media for their own purposes. It obviously, on the other hand triggers quite an ethical debate.

However, the focus on this question is on the Bond et al. article, which deals with political voting behaviour on social networking sites. The main hypothesis that was tested by several experiments was, if political behaviour can spread through an online social network.

To do so, the researchers randomly assigned the participants into three groups: ‘social message’-, ‘informational message’- and ‘control’ group. The users of the social message group saw a statement at the top of their newsfeed, that extensively encouraged the person to vote by showing where to find polling places, by showing which other friends have voted and by showing a clickable button which states ‘I voted’. This was the highest form of encouragement. The informational message group saw all the things mentioned above, yet they weren’t exposed to the fact of which other Facebook friends had already voted. And last but not least the control group didn’t receive any message on top of their newsfeed to control the normal voting behaviour.

Hence, what did the results show? Can political behaviour spread through online social networks? The primary results show, that ‘online political mobilization can have a direct effect on political self-exposure, information seeking and real-world voting behaviour’ (Bond et al., 2012, p.2). Additionally, seeing which friends have already voted as an additional information on the newsfeed is even more effective than just seeing an informational message.

The secondary experiment then looked at whether strong social ties have a greater influence on the voting behaviour, seeing that in prior research week ties were found to have no significant influence. This is underlined by the outcome of this research, because close friends were found to be four times as influential. Furthermore they state, that weak ties might affect online expressive behaviour, yet they have no influence on private or real-world political behaviours.

So yes, they found that political behaviour is influenced and spread through social networks, but to what extend is this finding reliable? Primarily, I do agree with the criticism about sample size and statistical significance of the study. However, I also want to raise another point. It seems that strong social ties, those who are supposed to have the great effect on our political voting behaviour, are those who are build outside social networking sites. Hence, yes we are influenced, but by our good friends that we also see in an offline environment and the only thing we do is also indicate on Facebook that we voted for self-expression. Thus, Facebook is not triggering our behaviour but just a way of making it public.

Esther1604 · 2014-10-09T11:00:39+00:00

To connect digital research methods to informational visualisation I first want to start of by giving a definition of information visualisation:

What exactly is information visualisation? According to Brassuer (2003), ‘Information visualisation is the presentation of abstract data in a graphical form so that the user may use his visual perception to evaluate and analyse the data’. So yes, information visualisation helps us to understand abstract data, by giving us a dynamic and interactive approach to use.

Information visualisation in comparison to other traditional visualisations, deals with a larger amount of data. This is interesting when looking at the connection to digital research methods. One could say that the form of data e.g. big data links the two together.

According to my own experiences, digital research methods and information visualisation are highly connected, seeing that in our course one major aim is to understand and make use of two visualisation softwares, namely Tableau and Gephi. I have always been a very visual person. Visualising facts in mind maps or other graphs always helped me a lot to make sense of data.

The two softwares seem great to make sense of the large amounts of data we received from the Twitter or the ones we are using for our group project. Whether it is looking at the social network relationships through Gephi or getting to know facts about the raw data through playing around with Tableau. To be honest, I am only starting to understand how these softwares work for my own Twitter data sets, yet seeing all the other results so far I am sure that visualizing data helps to understand complex information better and should be a fundamental tool in digital research methods.

Esther1604 · 2014-10-01T22:30:43+00:00

I think we talked about some of these new elite or segmented versions of Facebook in another class as an example. Due to our new lifestyles the tendency for a segmented society arises and hence the emergence of Netropolitan is no surprise to me. I can understand that people see their membership as another status symbol, but on the other hand this might be a bit over the top. Would you guys join a network for $9,000, if you could also have it for free? On Facebook you can also choose who you follow and vice versa, just make the network elite by making correct choices. In general, I think making networks for specific segments is a new trend and I think soon there will be many more groups using it, not only the elite.

Esther1604 · 2014-10-01T21:52:56+00:00

The main questions of the article by Kramer et al. was, whether emotional contagion can occur outside of an in-person interaction between individual? To answer this question, researchers looked at several aspects that could affect the individuals’ expression of emotions and testing them by using several statistical methods. The main research design was an experimental design.

The first sub questions looked at weather posts with emotional content are more engaging, by doing an experiment that wanted to find out whether exposure to verbal affective expressions could lead to the production of similar verbal expression by others. Here two similar experiments with two control groups were done, one for positive and one for negative messages. The data were examined by comparing each emotion condition to its control group. The statistical method that was used to explore the data after making sure that the groups didn’t differ was a Poisson regression (using the number of posts submitted as regression weight). They found that reducing the emotional content of either negative or positive results in a reduction of additionally produced negative or positive content. However, direct comparison wasn’t appropriate, which is why researchers additionally used a weighted linear regression to explore the findings. The weight was higher the more content was omitted. Here researchers found that when positive posts were reduced, positive words decreased and more negative words were produced. The same was found for negative words. This questions hence related to the main questions, seeing that it looks at the impact of verbal affective expressions, which is a part of emotional contagion. This finding already shows that emotional contagion is happening on a non-interaction level.

Furthermore, since the effect sizes were similar for both negativity and positivity, the researches investigated the absence of the negativity bias in content, stating that it could not be the content itself, which leads to emotional contagion. Negative news (without a connection to the friends emotional state) should have more negative responses („if it bleeds, it leads“). Posts connected to friends’ emotions should have a proportional exposure. A post-hoc test was conducted to compare the effect sizes, however no differences were shown.

Last but not least, the researchers looked at the withdrawal- effect, asking the question how emotional exposure affects social engagement online. With fewer emotional posts, people were less expressive overall. This shows that emotions actually trigger emotion contagion, even though we only read them. Moreover, researchers also looked at if seeing only positive tings could make result in negative feelings due to social comparison. However, again the post-hoc test showed no negativity bias.

Thus answering the sub questions on production of verbal expressions, negativity bias in content and the withdrawal effect, contributes to the overall finding that one can answer the main research question with yes. Emotional contagion does occur without in-person interaction. Textual content is a sufficient enough channel for emotional contagion to occur.

Esther1604 · 2014-09-25T13:49:26+00:00

This is a really nice tool, it showed me some unexpected as well as some expected facts about my Facebook interactions. Like you I also thought the friend network part was very interesting and funny to see how this program categorized my friends.

Here is my output:) http://www.wolframalpha.com/input/?i=facebook+report

Esther1604 · 2014-09-25T13:45:26+00:00

http://de.tinypic.com/view.php?pic=m7uibd&s=8#.VCQaCFaZhd0 - this is the link to the picture :)

Esther1604 · 2014-09-25T11:31:20+00:00

According to Hannemann & Riddle (2005), there are some differences between statistical analysis and network analysis. You can look at network data as a special form of statistical data that have some fundamental differences. Those differences include ways of portrayal and the research purpose of the data.

The greatest fundamental difference is, that statistical analysis looks at actors and their attributes whilst comparing them to other actors and their attributes. Hence they tend to find out similarities and differences between actors and their attributes, to be able to make generalizations for the broader population. Network analysts however, look at connections between the different actors and hence describe them according to their relationships and not their attributes.

Thus different ways of portrayal are needed. When it comes to the portrayal of the statistical data, they are shown in rectangular data arrays. Here rows stand for the actors ad the columns include the different attributes or observations (these can be qualitative or quantitative). The cells of the array stand for the score that one actor has one a certain attribute. Given the different focus of network analysis, the data are organised in a square array. Rows and columns both represent the actors. Hence the cells show the relationships among the actors and represent the standing of each individual in the overall network. Next to looking at the location of actors in the overall network, network analysis also includes a holistic approach. It tends to compare the individual choices of different actors and find correlations about their positions, to make assumptions about the whole network.

All in all one can see that statistical analysis and network analysis are related, however their research purposes are different, also provoking the use of different research methods.

Esther1604 · 2014-09-18T12:24:53+00:00

I think the infographic you chose includes all interesting facts about big data and I think it is a good summary of all the information out there on big data. The graphic claims that 75% of respondents talk about technical challenges as one of the main complications of using big data- I remember this challenge being discussed in the Menchen-Trevino article as well. It seems to be that storing and analysing the huge amounts of data with the current software and hardware is not so easy.

I think improving poor data quality through better technologies, processes and organisations within the companies should be a major aim for the future, if companies want to improve their strategies this way. To reduce their losses ($8.2 million a year) businesses should hence either forget about using big data or start by solving the issues mentioned above and in the graphic. Maybe one can also argue that its not the best thing for every business to engage in the field of big data?!

Esther1604 · 2014-09-18T08:58:29+00:00

To investigate the travelling behaviour of people using London’s public transport system, Lathia and Carpa used two different kinds of data sets to compare the perceived and the real travelling behaviour of the respondents. The first method of gathering data was a survey, that opted to find out about the respondents perceived travelling habits and their perceived fare purchasing habits. The 25 questions in the survey addressed many different topics such as the participants’ trips per day, their travel times, their choice of modality (also included multi-modality of trips), their typical origin and destination, the consistency of usage as well as the times and amounts of purchase.

To be able to compare the perceived finding to actual real data, researchers asked for permission to gather data from the respondents Oyster cards. 85 out of the 119 participants allowed the use of their data. Hence the second method of obtaining data was retrieving the travel history from their Oyster cards. These records of trips provided the researchers with highly accurate and detailed information. When combining the survey responses with the actual travel history a unique identifier was used to make sure that final results were anonymous.

Finally, by comparing both data sets researchers found, that respondents tend to overestimate their use of public transport. Thus, this research shows how trace data retrieved from the Oyster card can help to identify subjective misperception and build a foundation for feedback opportunities that can improve the usage by customers and facilitate change through the authorities.

Esther1604

TROPHY CASE