Understanding Digital Research

... is the place to be if you want to know more about digital research in the media. Welcome! We're Isa, Ellen and Mina and here we'll be sharing our own thoughts on different trends and topics alongside interesting links, photos and videos. Welcome and enjoy!

[isaminaellen]

Sleep Time is good time. At least for the most part.

Measuring my sleep cycle - now that was interesting. Although I have friends who do that on a regular basis, and I have to admit, I have thought about it myself, I was never too much into the quantified-self, tracking-my-every-move craze. Let alone post about it.

But here I am. Things change, I guess.

In order to track my sleep cycle, I downloaded the free (!) Sleep Time app for iPhone. I chose this one for two reasons: First, I picked it based on its features, as I wanted to measure the different phases of my sleep cycle (light sleep, deep sleep, etc.), and second - because it offered the option to extract the data as a text file. 

Downloading the data is a matter of a few clicks. You need to go to Azumio's main website, create an account (in 10 seconds max) and extract the data from the app you're interested in (they have a few more than just Sleep Time). For details, go here. 

So, I had my .cvs file in less than a couple of minutes, however, as it turned out, there isn't much I could do with it. The only data I was given was the duration of my sleep and its quality. 

Since there wasn't much to visualize, I thought it'd be nice to share at least some of the graphs generated by the app itself. After two nights of sleeping frighteningly close to my phone, this is the amount that I got. 

Less than 7 hours on Night 1 and almost 8 on Night 2, with an average duration of 7 hours and 10 minutes. Efficiency: 74%. I guess that's more than good for an overwhelmed Master's student. It is interesting to see that the beginning and the end of my sleep cycle are very similar on both nights, with an awake phase in the morning of about half an hour. Also, it seems like 2am is the time when I actually manage to fall asleep. 

From what the app indicates, I've had more deep sleep during Night 2, while Night 1 has its ups and downs. I assume that was the case because I went to bed late. Interestingly enough, the efficiency of my sleep was higher during exactly that night. 

I'd say 77% quality of sleep, given that I didn't have much deep+REM, is somewhat surprising. I'd be interested to find out more about how the app calculates that percentage. On Night 2, on the other hand, I only achieved 72% efficiency. 

At this point, I'm still not very sure how the app measures and calculates the quality of my sleep, and though I'm not a fan of sharing a bed with my phone, I'd actually like to use this app for another few nights. Tracking your own actions is somewhat daunting, but in the long run, it may actually teach us a thing or two about what we do right (and wrong) in our everyday lives.

[isaminaellen]

How did I sleep?

The last three nights somebody slept very close to me: my phone.

I tested the app "sleep cycle" to measure the depth, length and quality of my sleep.

It's very easy to download the app, you position your phone next to yourself on top of the mattress, possibly somewhere where you don't roll over during the night.

Though I am not very happy with the amount of data you can export from that app, its still very interesting what the statistics tell you in the morning:

(unfortunately this is in German, sorry, but I translated a few words):

You can see: I was in bed from 00:39 until 07:39 and had a fitful sleep.

Another disadvantage this app has: it only measures the time in bed and not the hours of "real sleep". The sleeping quality of this night on Monday was 63%: you can see the sleep quality when you export your data, because it needs at least data from 5 nights to show the sleep quality directly in the app.

Low sleep quality, not enough hours of sleep and awake at 3 in the morning.

Explanation for that: Assignment dues and living in an apartment with walls out of paper together with a Spanish flatmate who cooks at 3 in the morning in the kitchen next to my room on a regular basis.

What is interesting:

The sleep quality does not seem to be directly related to the length of sleep:

Though I had less sleep from Tuesday to Moday, the quality of this nights sleep is higher (76%).

Last night I had a very deep sleep, which could be related to the fact, that my flatmate didn't come home last night; But she did this morning: Guess when? 6 in the morning - you can see it in the statistics.

I tried to import the data in tableaux, but unfortunately not much was possible to do there with the data I could export through the app.

Maybe there is another way to export more data - would be interesting.

I think the whole quantified self movement has two sides: 

Yes, it is very useful for medical reasons and also on a personal basis to improve the fitness of your body or something like that.

I also read about apps which measure the emotions of your relationship and then tell you what to do or not to do.

In my opinion one has to be careful with that. We are human beings and  normally we should have intuitive feelings what is good for ourselves and what is not.

It may sound a little bit cheesy, I know: But if we more and more rely on apps like that and not ask ourselves how we think or feel about particular things, we could loose the sensitivity for our own body and our own mind.

[isaminaellen]

Tracking my own sleep cycle

I decided to use the free app Sleep Time by Azumio for Iphone. I chose this app, because it seemed like I could get the raw data out of the app and send it to myself via email. However, after receiving a zip file full of data, I had no clue what to do with it, let alone know how to clean the data and use it in Tableau.

The data itself is quite detailed, but does not tell me much about the quality of my sleep itself. Here is a screenshot of the zip file containing the data:

I then opened the highlighted data set in Excel (I tried them all), which is displayed below:

As you can see, this data gives me information on what the app itself was doing while I was sleeping. Which is seen in ‘alarm is triggered..’ and ‘startClockupdates’ for example. While this information is pretty interesting, I am not able to use it in Tableau.

The app itself produced some visualizations as well, so I decided to just share those. The first one is the classic visualization of a sleep cycle including both light sleep, deep sleep and times I was awake. And yes.. I went to bed pretty late both times using this app. This week has been stressful!

I really liked this second visualization provided by the app as well, as it looks like something I would have been able to do in Tableau as well:

I do not know if I would use the app a lot after this experiment. I do quite like using self-tracking data like this and don’t really see a problem with it or find it creepy like I have read people say on Reddit. I really liked seeing the visualizations of my sleep cycle and thus being able to see what the quality of my sleep was like. But I do not know how accurate these findings would be coming from a phone lying close to me in my bed. I am sure this has been tested, but I still question the outcome. It will for sure give you a good indication of what your sleep cycle is like though.

A thing I didn’t like was the so called ‘wake-up phase’. This allows you to set a time frame in which you want to be waked up. This means that if you pick a time frame of 20 minutes and you set your alarm at 08:00, you can be woken up ranging from 7:40 to 8:20. In this way the app can wake you during your light sleep, which would ensure you to be less tired when waking up. When I tested it I set my alarm for 06:45 and got waken up at 06:32. Now I have to admit I did notice that I woke up fairly easily, but those fifteen minutes are still too precious for me to let go off. Not a morning person! So if I were going to use the app again and I would have to wake up early, I would get rid of the ‘wake-up phase’.

All in all, the experiment has kind of failed, since I have not been able to get the raw data I wanted and put it in Tableau. But at least I can now say I have experience in using a self-tracking app!

[isaminaellen]

Facebook reacts to 17-year-old Malala Yousafzai winning Nobel Peace Prize

Like for many others, for me too, Facebook has become the place to keep in touch with friends, but perhaps even more so, to connect with colleagues and stay informed. A previous visualization of my network yielded exactly such results.

As part of my job as a journalist, I've been using the network as one of my main news feeds, thus following a number of national and international newspapers, magazines, and freelance reporters. As a result, my news feed is usually a mix of articles from different publications. It is not often that any of my friends and/or colleagues would start a political discussion themselves, but if they are interested in one, they'd repost a link that would then encourage a conversation. 

Last Friday, October 10, was an interesting day for following the developments on my Facebook news feed. It was the day when this year's (two) winners of the Nobel Peace Prize were announced, one of whom happens to be the world's youngest person to ever receive the award - 17-year-old Malala Yousafzai.

When there is large interest within my network on one particular topic, I often switch my news feed view from the "most recent" posts to the "most popular", in order to see how large that interest actually is. In the case of last week's Nobel Peace Prize announcement, Malala seemed to be the center of attention. When I ran through the most popular posts on Friday afternoon, every other post was about her achievement. 

I chose to look at several major media I follow, and eventually concluded that within the first five hours of the announcement, CNN had published three articles on the topic, the BBC had run two, Al Jazeera had three - followed by another two by the end of the day. The first of those articles were mainly an announcement of who won what, and why.

Each of the following stories by those international media focused on different aspects of Malala's life and her achievement. After taking a closer look at the end of the day, I noticed that some of those rather in-depth pieces (mainly profiles and interviews) were also shared by several of my Facebook friends. 

Of course, many of those posts were not short of comments. While most of those praised her achievement, others delved into the social and political implications of her winning. Yet, what generated perhaps the most buzz were Malala's own updates on the official Malala Fund page. According to a blog post by Facebook Media, within the first two days of the announcement, "11 million people generated 17 million posts, likes and comments about Malala". 

The numbers are impressive and this activity was certainly identified by many, including me. Because of the intensity of the discussion on Facebook, I decided to visualize the data from the Malala Fund Facebook page only on the day of the announcement, October 10. Thanks to netvizz, I extracted the necessary data and eventually had 454 posts, with 74,075 users liking or commenting 88,278 times. 

First, I opened the data in Excel and scrolled down to manually identify the most commented on posts. Once I had found those, I assigned them the numbers from 1-7, so I can more easily recognize them when I open the data in Gephi. The post with the most comments was post 3. 

And here's the visualization itself: 

Clearly, it is a directed graph, with several main posts that users are liking or commenting on, or both. The most popular post, number 3, is at the top of the graph, and the red ties are the ones connecting it to the users' activity. Here's a close-up: 

The next two close-ups show the ties between users and the remaining most popular posts. The left part of the image focuses on posts with IDs 2 (second most popular post), 6, 7, while the right part focuses on posts 4 and 5. 

To sum up, the achievement of 17-year-old Malala was well documented on Facebook, given that the most popular post (number 3) on her Malala Fund page generated nearly 2,000 individual comments. 

[isaminaellen]

Political conversations on my Facebook newsfeed?

In my first observation I could figure out two different things:

Firstly, I liked many Facebook pages of newspapers and magazines, which means I see current articles about political happenings in my newsfeed and hence can read the comments under this article or the content of the article.

Secondly, I have a lot of friends who are politically active or socially committed and also let their Facebook community know about this and their opinion.

  I scrolled down my Facebook newsfeed for the last 17 days and could notice:

Especially 4 of my Facebook friends are posting about the current happenings and ongoing discussions in the world everyday. They share links and respond to the comments under the shared links.

What is interesting is the fact that mostly people were commenting with the same or similar political view. They were discussing what could be improved or shared more information about a topic. Sometimes they had a different kind of view on a particular topic, but on a minimal level.

So no big political debates in my Facebook network?

I asked all 4 of them if they share their opinion openly, which means making their newsfeed accessible to all Facebook users or if it is just accessible to their friends or friends of friends.

As they are all having their private settings on “just visible for friends” it explains the findings above easily.

Most of their Facebook friends probably have the same political attitude or at least not a complete different view of things and therefore there are no or not many comments which contain an "argumentation".

It would be interesting if there are more findings in academic research to this finding, I am sure there are.

Let us go back to the Facebook pages of newspapers and magazines. Of course the comments show different point of views in the comments and of course people have different opinions about things (would be sad if it would not be that way).

  But this is what I observed:

(The following is put in very easy words and I know that I am throwing around with stereotypes, but there is a point)

The way of commenting or arguing with others, depends a lot on the level of quality and credibility of the (online-) newspaper or (online-) magazine.

If it is a “heavy” quality paper, the comments and argumentations between the commentators are of a higher quality than the comments under postings from a tabloid newspaper.

The latter has comments under its postings, which include politically extreme standpoints, swearwords and sometimes not a clear argumentation in their critical comments.

Whereas the quality paper has comments, which are more structured. The commentators are using more arguments, cite other papers to proof their point and tend to stay more objective.

All in all interesting findings, which would be interesting to research further in- depth.

[isaminaellen]

Assignment Group Project Update 2: Research question(s) and data visualizations

“You are not a product” (Ello Manifesto)

In its manifesto, Ello describes itself as an anti-Facebook social network, which does not sell ads and doesn’t sell data to third parties - hence doesn’t see users as a product.

Herewith Ello comes into play exactly at that moment when the criticism around Facebook and its use of “our” data is getting louder than ever before. For example Facebook will soon, according to their website, launch a new ad program that can read users’ browser history to deliver targeted ads specific to their interests (Kosoff, 2014).

With its invitation-only strategy, Ello appears to be different, maybe even better than the other social networks. Originally, it was designed by its creators as a private social network. However, in spring 2014 Ello became open to the public - or at least to those people with invitations (Mamiit, 2014).

Especially in the last two weeks, the number of members has exploded: “A report released earlier this week said that 4,000 users are signing up to Ello every hour. A second report released within the week stated that the rate had increased by seven times” (Mamiit, 2014). This report was published at the end of September. From that point on, there has been a snowball effect: One of the co-founders, Paul Budnitz, would not release user numbers but apparently Ello is now getting 50,000 sign-ups per hour (Hill, 2014). Therefore, we believe that the discussion Facebook versus Ello will go on for a while.

Because Ello’s supposed expansion is a buzzword not just in the mainstream media but on social networks as well, we have decided to investigate the following research question:

          RQ: How is Ello perceived worldwide?

Our findings would offer insights into how successful Ello really is and will be, and if it could potentially replace Facebook.

To explore this research question, we have received a Twitter dataset with the search term #ello, whichconsists of 984 unique tweets. During our research, we will look at a number of different demographic factors - e.g. age, gender and location, in order to get more concrete findings.

We have also decided to combine different tools for our data visualization to get as much precise visualizations as possible. During our preparations, we used Google Trends to first get an insight on how much is Ello being talked about. The use of Tableau helped us to assess where most of the tweets are coming from and what hashtags - and combination of hashtags, is being used with #ello. Gephi, on the other hand, helped us to understand the network behind #ello better.

In the weeks to come, we will also be working with the #ello LIWC dataset and since we also have our own Ello accounts, we will try to acquire our own Ello data from existing public accounts. 

Following are some of our main findings. First, we made a visualization of the time zones from which the tweets containing #ello were sent. We added a filter to make sure no records below 5 would show up and several tweets containing no time zone had to be deleted to arrive at this result:

The five biggest circles make it clear that most tweets were sent from the US & Canada time zone. Other time zones, like London, Berlin and Tokyo, are also represented, but are not nearly as big as the US & Canada time zones. However, time zones like Lisbon, Helsinki, Berlin, Paris, Amsterdam, could be grouped together as these cities all share the same time zone. This would make the distribution look very different. 

To make it more specific, the next visualization gives a more in-depth look in which cities most tweets containing #ello were sent from.

As expected, most cities represented in this visualization are in the US. This makes sense as most tweets were sent from US & Canada time zones. You could wonder if this means Ello is that much more talked about in the US or that US tweeters are just more likely to link their twitter to their time zone and city, since we once again had to exclude quite a few tweets without any location data.

The next visualization shows the language in which Ello is most talked about:

As you can see, most communication on Ello is in English. Seeing as Twitter itself is quite internationally orientated and Ello is an American company, this is no big surprise.

What’s more, we decided to look at the search terms that #ello has mostly been associated with. First we need to note that the two most tweeted hashtags have the meaning but are capitalized differently by different users (i.e. #ello and #Ello). There is also one spelled as #ELLO. Although Tableau allows us to merge those two, we’ve decided to leave them separate, so we can later visualize the relationships between those two and all other search terms (see next graph). Also, what we can take away from this visualization is that original tweets mainly used #ello with #UPDATE and #NEWS. On the contrary, these two combinations were barely “favorited” or “retweeted”. In fact, the most retweeted combination included more than three terms, among which #Ello, #viral and #socialmedia. 

The following graph, made in Gephi, builds on the observations we made above. Here, we have left the different capitalizations of #ello (#Ello). For the sake of clarity, we have filtered the visualization to show only those nodes that have five or more ties within this network. It’s apparent that #Ello with a capital “E” is the one used by the most users. Unlike #ello with a lower-case “e”, it is used together with #socialmedia and #elloinvitecode.

In order to give an even better-rounded overview of the way our search term was used on Twitter, we decapitalized all variations of it. Here’s what our graph looked like.

Again, this graph represents the nodes that have five or more ties within the network. Except for #ello, we have highlighted some of the other frequently used key words. Among those are #viral (just like the Tableau visualization indicated), #network and #technology. All three of them are to be seen on the left of the graph. Other often used hashtags include #facebook, #socialmedia and somewhat less so - #socialnetwork.

Conclusion

Based on our visualization, we have already found out a lot about Ello’s followership. Of course, there’s more to be discovered. One main question that we have is, is it possible to combine positive and negative tweets (after visualizing our LIWC dataset) with different demographic factors in one graph?

References

Hill, K. (2014, October 1). Ello vs. Facebook. Forbes. Retrieved from http://www.forbes.com/sites/kashmirhill/2014/10/01/facebook-vs-ello/

Kosoff, M. (2014, June 13). Here's How To Opt Out Of Facebook's New Plan To Sell Your Browser Data. Business Insider. Retrieved from http://www.businessinsider.com/how-to-opt-out-of-facebook-plan-to-sell-your-browser-data-2014-6#ixzz3FfLxpNbv

Mamiit, A. (2014, September 27). Ello: Here's why the new social media site is generating so much buzz. Tech Times. Retrieved from http://www.techtimes.com/articles/16608/20140927/ello-heres-why-the-new-social-media-site-is-generating-so-much-buzz.htm

[isaminaellen]

Sentiment analysis for #digitaljournalism in Tableau

Of all the tasks thus far, visualizing my LIWC dataset in Tableau has been the most challenging one. In a nutshell, it involved editing text files, writing Excel formulas (which should not be a big deal, but for a total Excel non-expert like me, it was somewhat of a battle), creating different sheets and merging those. It was all good until I imported my newly created Excel file into Tableau, and all my data disappeared. After going over the instructions time and time again, I figured out which step (in TextWrangler) I had missed along the way. All things considered, I was eventually able to make my visualizations. 

First, I was curious to see what combinations #digitaljournalism was used in, in a positive context. 

It turned out that #digitaljournalism and #contentmarketing was the most popular, positive combination - though only recorded four times due to the small size of the dataset. Interestingly enough, this combination was also the one used most commonly with a negative connotation. Again, the number of recorded instances is very small. 

Afterwards I looked into the different locations that users tweeted from, and the number of positive and negative tweets in each of them. 

The most tweets with #digitaljournalism came from England, with 2/3 of them having a positive connotation and the rest - a negative one. Amarillo, Texas is the only other location that contains negative tweets. This time they are just as many as the positive ones, though the number of overall tweets is smaller than that from England. Users in all other locations have tweeted positively on the topic.   

At the end, I also decided to look at the number of retweets in the dataset. Of all 20 retweets, 12 were positive and 4 were rather negative, 

Overall, we can see that #digitaljournalism is used in a rather positive context. 

[isaminaellen]

LIWC Workshop

I tried to get a visualization of my data set for more than 3 hours- without success.

I could not combine the datasets in Tableau, though I tried everything what my classmates suggested on reddit.

I removed .txt and saved it.

I entered the formula and "1" appeared. I don't know how exactly it should look like, but it looked correct to me according to Erickas description.

--> 

I imported the dats sets "tweets" and "users"

But when it came to Tableau, it would not work out: 

Maybe there is the chance to look at this together on friday or time to ask questions what I could have done wrong.

[isaminaellen]

Time Warner Cable Uses Big Data to Optimize Viewer's Experience

As there will be guest speakers in class on Friday talking about how digital data is being used in “real business life”, I asked myself how companies are actually using big data or data visualization tools.

Since I have been working in television for two years, I know that for example the viewing-figures/ratings are visualized with tools comparable to Tableau.

But besides that, what could be the use in media related companies? I did a little bit of research and found an article/interview on “How Time Warner Cable Uses Big Data to Optimize the Viewers Experience”.

As you probably know Time Warner Cable (TWC) is an American telecommunications company, and not a small one - they have over 14 million costumers.

Hence Times Warner Cables has access to a lot of data and yes, they use big data tools in order to adjust their infrastructure to the changing wishes of their customers.

The data provides an insight in what their customers are looking for and they can easily create different profiles out of that.

Personalized Advertising

Joan Gillman, President of Time Warner Cable Media, explained that they combine public data sets such as real estate records, demographics or voter registration records with local viewing habits.This allows them to create advertising campaigns that are highly targeted.

TWC also does multi-channel advertising: Big data techniques are being used to measure the engagement of users on each individual platform and can adjust an ad campaign on each platform if necessary.

Detailed metrics lead to detailed information

For cable companies like Time Warner Cable, aggregated user data is very important. With data they can optimize their network and programming.

Normally TWC knows – thanks to big data - how often costumers use what service (OTT services, interactive TV or mobile TV); So it is easier for them to interpret information about peaks in their network demand.

Time Warner Cable uses Alteryx to help them understand how their viewers watch their programming as well as their advertising clients performed.

Thanks to their data analysis in the last years, Time Warner Cable was able to perform cross-platform analysis in order to predict which homes would be interested in what movies via their Movie on Demand platform. Thereby they can increase their sales.

This company understands that data is inevitable in the process and has successfully used to find new revenue streams, improve their marketing strategies as well as their network infrastructure.

   More interesting case studies can be found here: Case studies

I summarized the article; the previous content is not my intellectual property.

[isaminaellen]

How do people feel about Costa Coffee? Sentiment Analysis using Gephi

I decided to use hashtagEdges because that was one of my focus points in the Tableau visualizations as well. However, I’m not sure if I made the right choice and I will explain why.

This is what my entire hashtag edges data looks like in Gephi..

As you can see it is very spread out. Almost to the point where you can barely see the nodes and edges when trying to get everything in view. This has to do with the fact that there is no one hashtag that is used very often. This is why it might be a better choice to look at UserEdges or rtEdges.

 I zoomed in on a couple of nodes to get a better view. I started with the biggest and most connected option: 

This is the most retweeted tweet and the hashtags are thus most often used. Sadly, the tweet is not associated with any positive or negative emotion. The purple indicates that there is none: two zeros in both PosB and NegB.

But there were of course hashtags that did get linked to positive or negative emotions.

This tweet by Jackhowson96 shows how he uses the hashtags #FlatWhite and #Forever in a positive way. These hashtags were featured in my tableau visualizations as well.

This tweet by hayleypatrick shows negative emotion regarding the hashtags #CostaSwanley, #dairyfree, #glutenfree and #cherrybakewells. Again, this tweet is seen in my Tableau visualization as well.

Sadly, the hashtags were very spread out and not really connected to each other. Those that did seem more connected, did not have an emotional aspect to them. Using UserEdges or rtEdges might have given a better result. Next time I’ll do this, I will just use a completely different hashtag and hope the outcome will be better!

[isaminaellen]

How do people feel about Costa Coffee? Sentiment Analysis using Tableau

To get this graph I had to remove the biggest ‘bubble’ out of them all: the tweets containing no hashtags. This one consisted of 47 tweets with 30 of them positive. However, I wanted to look at the hashtags alone, so I decided to remove this bubble. This left me with this visualization:

Since I removed the bubble with records without hashtags, the hashtags that are left only come from 1 tweet. The effect of this is that there is no real distinction between which tweet is more popular than the other. However, I can look at what hashtags are related to positive emotions. Hashtags such as #relaxing, #treat, #lovecoffee are all used in a positive manner. Which makes sense. However, I feel like one hashtag does not belong in this category, and that is the #sortitout hashtag. This is not used in a positive manner at all, but is categorized in this way. I guess this shows the downsides of sentiment analysis where it is hard to take context into account for example. 

I then proceeded to do the same for negative emotions connected to the different hashtags. I once again removed the bubble with records without hashtags, which consisted of 47 tweets with 5 of them classified as negative:

This once again caused less variation in my outcome, but we can see that negative emotions are used way less than positive emotions. Only two tweets are associated with negative emotions. I once again question whether #WimpyKid should be classified as negative.

The next thing I looked at was positive emotions relating the location of the user tweeting Costa Coffee. This can be important, as Costa Coffee might be more popular in certain areas of the United Kingdom than others.

When looking at this graph it is clear that most positive emotions are connected to the locations England and London. England has 17 records and 9 positive tweets, which is quite a lot. However, the fact that the location England or United Kingdom is used, does not give much insight in which areas of the UK Costa Coffee is perceived more positively.

I did the same thing for negative emotions:

This shows that again, there are not many negative emotions. Only both England and London had 1 tweet associated with negative emotions.

Another simple bar graph shows this distribution of negative and positive emotions. Out of 79 records, 44 are associated with positive emotions and only 7 with negative emotions:

So to sum it up, Costa Coffee does not seem to be attracting many negative tweets on Twitter!

[isaminaellen]

What terms go hand-in-hand with #digitaljournalism?

Since both my personal and professional interests have a lot to do with new technologies, journalism and innovation, I decided to work more closely with the Twitter dataset for #digitaljournalism. In particular, I went on to visualize the relationships between #digitaljournalism" and other search terms.

What caught my attention from the beginning was that the search term "digitaljournalism" was spelled in three different ways - #digitaljournalism, #digitalJournalism and #DigitalJournalism. Though content-wise all three of them represent the same thing, my guess was that in a data visualization tool like Gephi, they would be treated as separate entities.  

It turns out there were indeed three clusters with the main node being #digitaljournalism - in one of its three variations. The most used spelling was the one with no capital letters in the search term, and I assume, if I had the complete dataset, there would have been even more nodes connecting to it. Also, there may have been another spelling variation, hence at least one more cluster. 

As it turns out, however, there is a pretty simple to strip all search terms of their capitalizations (Textwrangler to the rescue!) and unite the three variations into one. Below is the resulting visualization: 

My main learning from this exercise was rather practical, as I now know how to apply different filters in Gephi in order to get a clear and comprehensive visualization. 

[isaminaellen]

I’m a journalist. I’m (finally) getting my Twitter on

This post was originally published here. 

I’ve had a Twitter account since November 2009 but until last year, that was all it was. An account. Two tweets in total, six proud followers (who were kindly forced to follow me), succeeded by plenty of inactivity. Yet, last year, I made myself a promise to change that. In fact I documented it in a blog post and swore to “power through the #- and @-madness and to join the never-ending conversation.” (You can read the rest of that oath here).

Now let me be very honest here: I am not the most avid Twitter user, and one look at my account would most likely already reveal that. I’ve posted all in all 278 tweets, I’m following 253 people and organizations, and in return, I’m being followed by a mere 162. I know. I’m a journalist, I need to get on top of my tweeting game. I will.

I took a step back this week to think about what I’ve done to increase my followership in the past and what I can do to further develop it and sustain it in the future. I’ll start with the relatively recent past.

Tweeting at events

At the beginning of May, I went to the International Journalism Festival in Italy and volunteered in their press office for five days. For me, there was probably no better place to do what I love to do and make new virtual (and real) connections. As a result, my Twitter history tells that I sent out 17 tweets, earned myself 14 retweets, 18 favorites and 7 new followers. I guess for a Twitter newbie that wasn’t all that bad.

Morale of the story: Events are a great place to start tweeting.

Speaking of which, I attended another one at the end of the same month. It was an entrepreneurship/digital tech conference in Bulgaria where I went as a freelance journalist. To compare, it was only a two-day event, yet I stood proudly behind the following Twitter activity: 23 tweets, 17 favorites, 8 retweets and 8 new followers.

Overall, in the week of the entrepreneurship event, my Twitter activity looked like this:

(Make a note to start using SumAll to keep track of your Twitter history).

Just a week after the conference, the number of my overall activity had dropped significantly, yet I was still gaining new followers. I’d guess they followed the buzz from the previous week as well as the article that I published a few days after the event.

Picking out a number of #-s to follow

Of course, I cannot be at an event every month, so what I’ve been doing in the meantime is tweet, favorite and retweet posts on the topics that I’m professionally interested in. That way I could find out for myself who the opinion leaders are in my field, whom I should follow and strive to be followed by. Among my most used hashtags are #digitaljournalism, #contentmarketing, #startups, and #CEE, to name a few.

To increase my tweeting efficiency, I’ve been using a scheduling app called Buffer, whose free version does more than enough for my own Twitter needs.

As a result, I’ve seen a slow but steady growth in my follower base. Since the beginning of June, their overall number has gone up from 120+ to 160+ and I have added another close to 100 accounts to the list of those I follow – about 250 of them at the time of writing.

In fact, in the past 24 hours, I’ve been tweeting more actively with the hashtags #datajournalism and #dataviz just to see if such a targeted approach would yield increased interest from potential followers. The fact is, my 8 tweets in this time span have resulted in 1 retweet, 1 favorite and 1 new follower (with whom I cannot necessarily establish a common interest in #datajournalism). Based on those results, I have two main observations.

1. Twenty-four hours are certainly not enough to engage a potential Twitter audience, and 2. since discussion on rather broad topics like data journalism or data visualization are not constrained to a specific time or place, it takes longer to find the right people to follow in the field and in turn to get them to follow you back.

Obviously, what I am doing to increase my Twitter presence is just a fraction of what can and perhaps should be done. For inspiration, go here and here. I’m certainly on it.

[isaminaellen]

Anybody want some coffee?

This week’s assignment asked us to do an experiment on one of your social network sites. Since I already had the Costa Coffee theme for my twitter data set, I decided to just stick with this coffee theme! For two days straight I liked everything coffee related I saw on my Facebook feed.

First of all I made sure to like the Facebook pages of two coffee places I myself quite like: Starbucks and Costa Coffee.

After liking the pages, I immediately saw older posts and recent posts by these companies appearing on my newsfeed:

Not that shocking yet though.

When I proceeded to like the posts I did not really feel like it made an immediate difference to the content on my Facebook page. 

However, after liking quite a lot of posts, a Buzzfeed post containing a picture of the very popular Pumpkin Spiced Latte by Starbucks showed up on my feed. Now, this could have been a coincidence, but since the post dated back to September 5th, I wonder if it is indeed because I liked those coffee related posts. A friend of mine liking this exact post all those days ago might be related to the appearance of the article as well.

Another part where I found changes was in my recommended pages. Starbucks related pages definitely became more prominent:

All in all, I don’t feel like liking a lot of posts on coffee really changed the content of my Facebook feed that much. I did not get many new posts relating to this topic, except for the Buzzfeed article, and posts that did show up were posts made by the two Facebook pages of Starbucks and Costa Coffee I just liked two days ago. I think the main thing that changed is seen in my recommendations of certain pages, something that does not really change the content of a Facebook feed to a great extent.

In this experiment by Matt Honan where he liked everything on Facebook for 48 hours straight, he came to the conclusion that Facebook definitely does filter out the things it thinks you want to see versus letting you see everything. This is true, since the people you see on your newsfeed are mainly ones you either always talk to or like a lot of stuff off. For this reason I think that if I were to keep up liking these coffee brands and pictures for a longer period of time, I would start to see a bigger effect.

But then again, I’m not too sad about the experiment not working to its full extent, since I now will not have to deal with a Facebook newsfeed cluttered with coffee images!

[isaminaellen]

Interview with Lisa Hönig: About quitting Facebook and coming back.

Lisa is living in Munich and is 25 years old. Besides her studies she is working in the media agency Textbau as a media consultant/ journalist.

Lisa, what were the reasons that made you „quit“ Facebook? 

Realizing that most of the content people share on Facebook is not necessarily content they themselves like, but content they think others will like (or that will "generate" likes via the like-button, i.e. the social beer game of whatever it was called), which makes Facebook a bit like high school, with all its peer pressure groups. I was starting to feel too old for that, which was one of the reasons I quit.

It's no accident that "to quit smoking" and "to quit facebook" reveal similar structures of addiction. Whether you like it of not, you become addicted to the neverending stream of information Facebook provides. Once I realized that, I decided it was time to quit.

Or take friendships as an example: it was becoming increasingly difficult for me to figure out who the important people were in real life. I wanted to see if the people that were very actively engaging with me online would still be in my life once I went offline.

What did you miss during your Facebook-abstinence and did you have positive experiences?

I didn't miss anything. And that was probably the most positive experience: as long as you're still on facebook, you imagine you will miss out on so many things, but you really don't. Cutting off the constant stream of information not only improved my ability to concentrate a lot, it also forced me to only stay in touch with people that really matter, and to spend more quality time with them.

What happened that you decided to „come back“?

I really only came back for organizational purposes: I'm taking part in the World Model UN simulation next year, and my university's delegation does all their organising via a facebook group. Additionally, I currently work in a field that relies very heavily on social media, so I have to be present on all the "channels" (facebook , twitter, etc.). However, I am thinking about creating a FB account purely for work purposes and deactivating my personal account again.

[isaminaellen]

Social Networking Behaviour. Do I even have that?

I guess everyone who has signed up to a social network is acting and reacting with it and automatically has a social networking behaviour. But sometimes I think I am REALLY backward regarding social networks.

I am using Facebook.

And checking my mails with Googlemail.

And...that was it. Well, reddit and tumblr came into my life 3 weeks ago.

I am not using Instagram, Twitter or something else. I actually can’t name the reason for that. Maybe I just don’t want to be available on different platforms because this automatically means that you spend more time on that and I am not willing to give more time. 

The second reason is, that with the social network services I am using, I actually have everything I need. At least I thought that, until I read Nienke's post here - now I have to think this over.

I am not highly active on Facebook: I post photos and status updates once or twice a month, comment on photos approximately two to three times a week and like posts, photos or comments approximately every second day.

The first time I actually realized that Facebook seems to use algorithms to invade my newsfeed was when I changed my relationship status. All of a sudden I got invitations to wedding fairs in Munich, bridal shops (not even engaged btw!) or special offers on engagement rings.

As a sacrifice for DRM I misused my Facebook account to like almost every post or photo my active Facebook friends were posting. I did not inform them about this experiment before intentionally.

I could classify two reaction patterns:

Out of the reason that I am normally not liking or posting that much on Facebook, my Facebook friends got very confused. I got a lot of messages asking if I am ok or informing me that somebody probably hacked my facebook profile and that I should take care of that.

Facebook friends whose posts or photos I liked a lot, started to like back. They took old posts or pictures on my wall and started to give likes on them.

This shows:

1)  that everybody has a specific behavioural pattern on Facebook and if you break out of this, some of your Facebook friends may notice.

2) that likes can be a form of „payment“ on Facebook. I give you a like, you give me a like, you just have to look at #likeforlike on instagram for example. Not for nothing there are dubious companies selling likes for companies' Facebook sides or content of private profiles.

Quitting Facebook. Is it even possible? I could not do it myself (it is the only social network I am using- at least I will keep that).

But I think it is interesting how people experience it. Thats the reason why I interviewed Lisa. She quit Facebook for 4 months from February 2014 until June 2014. Read the full Interview here.

[isaminaellen]

Visualization of Twitter data set with the help of Tableau

The set of data I used for this visualization consists of 100 tweets with the search term #greenpeace.

The first visualization shows the time zones where #greenpeace was used the most. There are many tweets that are marked with none, which means that these posts are not connected with information about the particular location.

As you can see in the visualization the most tweets are posted in the area of US & Canada (Pacific Time and Eastern Time), but also from Brisbane, London and Tokyo.  

The following visualization shows which apps or service people tweeting about #greenpeace are using.

Twitter for Android and Twitter Web client are the most popular.

The last visualization shows the hashtags most used in the combination with #greenpeace.

For example:

#Japan #SaveTheWhales #Sharks #bees #birds