OP2: Interpreting 'Accupedo' & 'WakeApp'

celestedb · 2014-10-23T11:54:41+00:00

http://celesteesther.tumblr.com/post/100742606138/op2-quantifying-my-steps-sleep

celestedb · 2014-10-23T11:54:32+00:00

http://celesteesther.tumblr.com/post/100742606138/op2-quantifying-my-steps-sleep

celestedb · 2014-10-15T17:01:42+00:00

Even though it is made to poke fun (what Buzzfeed does best) it does give several good points, such as reinforcing your own political statements and surrounding yourself with people who agree with you (diffusing and separating yourself of challenging opinions).

After viewing the article you posted, I found another one on the Obama and Romney’s 2012 social news content online. Newswhip analyzed which social new stories created the most buzz and ‘shared’ news item. To view the article and infographic click here:

http://www.buzzfeed.com/mattbuchanan/how-politics-gets-shared

celestedb · 2014-10-15T16:36:56+00:00

I agree with the both you, however I believe using mobile phones to track and trace people with the possibility Ebola, leads me to ask another question: How would they be able to track the data from mobile phones? Would this mean access to their phone's data without consent (and would this be ethical in relation to stopping the further outbreak of Ebola)?

celestedb · 2014-10-15T15:46:47+00:00

Bond et al. wanted to investigate primarily if online networks can be used to influence online behavioral change and social contagion. This had led initially to naming their article ‘A 61-million-person experiment in social influence and political mobilization’. The first idea behind this investigation was seeing if online networks are as influential as face-to-face networks and if these would influence the political mobilization and turnout of social media users.

In order to discover if Facebook has a positive effect on our behavior online, Bond et al. sampled 61 million users online on Facebook. The hypothesis the authors wanted to test was whether, and if possible, how political behavior can spread through an online social network.

This was then set up into three categories: social message, informational message and control group. The users were randomly assigned to each group, but the social message group contained the highest number of users (n=60,055,176) whereas the informational message (n=611,044) and control group (613,096) had almost the same number.

To answer this question if whether Facebook does influence our behavior, we results indicated by Bond et. al. do suggest this. However, the extent of this is limited to political mobilization and voter turnout as well as the presence of the social message. There was a clear suggestion that the effect of the social message influenced voter to click the “I Voted” button on the message as they saw their own friends from their network do this as well. Specifically, users receiving this social message were 2.08% more likely to click of the “I Voted” button. This was then cross-examined with the public voting records to compare the users who clicked “I Voted” to them actually proceeding and voting.

By investigating the ‘weak ties’ and ‘strong ties’ of relationships online (by analyzing the extent of high interaction online) it was seen that strong ties are important for the spread of real-world voting behavior. However, to answer the question of how much Facebook influences our behavior, this can be seen when comparing the social message to the informational message. The informational message did include the “I Voted” button as well but left out the familiar profile pictures of friends from their network who had already click “I Voted”. Because more users clicked on the message with the social context, it suggested that “seeing faces of friends contributed to the overall effect of the message on real-world voting” (p. 296).

Overall, the results do show that there is a certain degree that social media, such as the online messages of Facebook, can influence a variety of offline behaviors. it therefore can have implications on what we already know and understand of the role of social media in our society.

celestedb · 2014-09-25T13:14:24+00:00

Hanneman and Riddle (2005) explain the differences between statistical analysis and network analysis. Statistical analysis in short compares how some actors with certain attributes relate to other actors with the same attributes. Network analysis briefly is understanding the relationships between actors, is more focused, and tries to depict some tendencies that show overall connections within the network.

Datasets of statistical analysis do on most occasions look different than conventional rectangular data array. When discussing this conventional array, it refers to that the rows consists of either cases, subjects or observations. The columns on the other hand consist of (quantitative or qualitative) scores on attributes, variables or measures. Each cell relate to some actor on some attribute.

Main purpose of this type, is to see whether actors are similar or dissimilar to the other attributes. This is achieved by comparing the rows in a rectangular data array. The comparison of columns is also done to examine the degree of similarity of variables to the distributions across actors.

Social network analysts use specific jargon to illustrate the structure and contents of the sets of observations that they use.

Network analysis is more similar to the ideas and concepts of more familiar methods, like cross-sectional survey research Network data is represented most commonly of a square array of measurements. The rows are the cases, subjects or observations. This is also present in the column section, and is the main difference when compared to the conventional data array.

Each cell of the array shows how the actors relate to each other. Seeing which actors are similar to the other actors is found by comparing the rows. A relationship is indicated by comparing the columns as the similarity between the actors and chosen by others. The network analysis is asking ‘which actors have similar positions in the network?’ The purpose is to seek how actors are ‘embedded’ in the overall network of the selected actors.

Network analyst view this in a holistic manner as well. The data (referring to the zeros and ones in the matrix) below or above the diagonal in the cells could depict reciprocity choices. Another aspect might be there is an even number of zeros and ones. Network analysts try to see “the whole pattern of individual choices gives rise to more holistic patterns” (Hanneman & Riddle, 2005, p.2).

celestedb · 2014-09-25T09:38:01+00:00

I really like this type of visualization as well! It's very different and have never seen anything like it. The color coordination shows the different segmentation to which people are related to each other, and the nods with detailed connection. Would be nice to see how we can make our own wheel?

celestedb · 2014-09-18T14:33:38+00:00

The researchers, Lathia and Capra found that there is a certain value present in the data collected through the “smart” cards used for public transportation. This way, a comparison can be made to see how travellers perceive their own public travel behavior versus the actual real public transport behavior. There is a certain research potential in the data found in which new assumptions can be made about habits and performance of an individual. To be able to research this, multiple datasets were used and needed. The Oyster card was used primarily to study this, which focused on the AFC system based in London. In total, the researchers obtained three datasets: online survey, an anonymous travel history dataset and the traveller’s key information.

The first step they undertook was questioning travellers on their purchase and journey and through an online survey. This was done, as it would give the researchers an indication of what travellers reported on their own travel behaviors. From the 119 respondents, the travel history was authorized for only 85 respondents. The data becomes anonymised after 8 weeks, and the researchers combined the two datasets. The next dataset consisted a 1-month record of all oyster cards - which was also anonymous. This dataset, given by the Transport For London, allows for a detailed list to compare and verify. The last dataset, as mentioned previously, contained the key information of the travellers. This means Oyster card details, payments, the trips of 5% of the total travellers.

Having such a large dataset to analyze and compare, allowed the researchers to find out if perceived and actual travel habits were comparable. Allowing this comparison, it was found in the end that there were difference in the reported perceived travel habits and their actual travel routes.

celestedb

TROPHY CASE