Data collection on LinkedIn and Twitter

As identified above, a LinkedIn group and a Twitter hashtag campaign were two of the most important communication channels of the Onder-Tussen initiative. Therefore, data were collected from these two online social media sites. This research investigated methods to measure social capital in online social media networks. Data sources were, thus, online social media tools. In this case, data were collected from LinkedIn and

Twitter. Therefore, the researcher decided to observe, analyze, and interpret online social media data to implement and evaluate the methodology. Furthermore, the researcher

49 chose a descriptive approach to identify and evaluate indicators which were used to measure social capital in these online social media networks (Hennig et al., 2012).

To be able to answer the research questions properly, the online social media sites of the Onder-Tussen initiative were investigated. The Onder-Tussen initiative uses a LinkedIn group (http://www.linkedin.com/groups/ONDERTUSSEN-3818086) and a specific hashtag on Twitter (#blt020). For the purpose of this research project, especially these two social media tools were used to gather relevant communication data.

These communication data were the expression of the otherwise intangible concept of social capital. It became actually visible, and therefore, observable in the first place. The subsequent sections 4.2.1 and 4.2.2 delineate how the data sets for the

implementation of the research methods were created and which data were included in these sets.

4.2.1.The LinkedIn data set

The LinkedIn data set was created on the basis of the LinkedIn group of the Onder-Tussen initiative. The LinkedIn group was founded on March 8, 2011 and had 282 members in October 2013, and 287 members in July 2014. The first post in this group was uploaded on March 13, 2011. The founder defines the type of the group as networking group and describes the group´s objectives as follows: “ONDER-TUSSEN: Shares knowledge and practical experience about temporary use of wastelands.” (http://www.linkedin.com/ groups/ ONDERTUSSEN-3818086). He encouraged (future) members of the group to contribute to the network and to invite others to do so as well. Anyone can become a member of the group if he or she has an account on LinkedIn. Posts and comments – so- called discussions – are publicly visible. People, therefore, are also able to follow the discussions passively, even if they are not logged into LinkedIn. Data were gathered manually from the LinkedIn group in October 2013. The researcher gathered information about time and date of the discussions, the type of text (post or comment), likes, and the individuals involved in the discussions. Individuals were identified either as government professionals, or other professionals, and citizens. Furthermore, the locations they work or live were categorized either as local (in and around the Amsterdam region), or non- local. These characteristics were added to the data set.

Additionally, several types of relationships (edges) between sources and targets were defined. In this investigation, a relationship is defined as an individual reacting to another individual. This reaction is supposed to imply a certain value expressed, such as “I think what you say is important, thus, I react to it.”. Furthermore, all unique individuals (nodes) were identified. For LinkedIn, this meant the identification of authors of posts and reactions they got from others who commented on their posts, or who liked their posts. Therefore, in the LinkedIn data set, two types of relationships were defined: reactions to a post and likes of a post. This section illustrated the structure of and the data included in the LinkedIn data set. The next section 4.2.2 describes how the Twitter data set was structured and which data were included in that set.

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4.2.2.The Twitter data set

This section describes how the Twitter data set was created based on the Twitter hashtag campaign of the citizens´ initiative. The Onder-Tussen initiative uses the hashtag

“#blt020” on Twitter when referring to interesting information about the temporary use of wastelands. The “blt” in the hashtag stands for “BraakLiggende Terreinen” and is the Dutch term for wastelands. This hashtag was first used on September 20, 2011. The researcher executed a Twitter search for the hashtag #blt020 and extracted the relevant data manually. The following data were gathered: time and date of the tweets, the type of tweet (tweet, retweet, reply, etc.) and the individuals who posted and/or were mentioned within tweets under the hashtag #blt020. By manually adding replies which did not necessarily contain the hashtag, the Twitter data set was extended in addition to the data of the Twitter search. Furthermore, the researcher extracted and followed all links that were posted in the tweets gathered.

For the Twitter data set, the classification of relationship types was different due to the medium characteristics of Twitter. Twitter users can use mentions in their tweets, they can easily retweet something, and they can directly reply to a tweet. All those actions indicate a relationship between Twitter users. Therefore, the following types of relationships were defined: direct mentions in a tweet, retweets, and replies. As all collected tweets were classified manually by the researcher, an additional relationship was identified. Twitter users might also mention other users in their tweets by retweeting someone else´s tweet. Thus, it might be the case that the mention in the tweet was originally made by someone else. Although this was only observed rarely (9 times), this original mention type of relationship was added to the classification.

Different types of relationships are different in their quality as well. Liking

someone´s post is less demanding or involving than actively creating a reaction to a post. Additionally, a direct mention, as it is sent via an own tweet or via a retweet, is more directed than just retweeting something. A direct reply also is expected to claim higher involvement than a simple retweet. Thus, the focus of these relationships lies on the individuals and their interactions with each other rather than on the content of the tweets and discussions. In the online social network analyses, the individual view was surmounting the message or content view. As the structures of and the data included in the LinkedIn and Twitter data sets were described above, it is now important to illustrate the actual implementation of the three research methods introduced in Chapter 3. The next sub-chapter 4.3 explicates the choice of using the network visualization and analysis tool Gephi for further analyses, which are also referred to in the following sections.