SUB CAPÍTULO I: LOS ALIMENTOS EN EL DERECHO PERUANO
1. LOS ALIMENTOS DEFINICION
1.9. ALIMENTOS PARA MAYOR DE 18 AÑOS
The goal of our research was to develop a framework to categorize communication strategies used on social media by organizations in the public sphere.
It can be concluded that there are three different strategies that are often used on social media in the public sphere. Of these three strategies, the strategy with the most
interaction with the follower works best if growth on Facebook is the goal of the organization. If the goal of the organization is to simply be present on Facebook while not investing any time or money, the disclosure strategy is probably best used. The information dissemination strategy is best used a bridge between both strategies, when a shift is network growth or a larger following is desirable, for instance.
Within the networks it can be concluded that a regular posting bases, keeping
discussions alive and using specific content for Facebook can lead to more followers. Simply posting content to an organization’s own channels, in order to redirect the reader of this content, does not always give growth within the network. People want to be interacted with by the organizations, when they follow this organization on
Facebook.
When all an organization wants is to spread its message without using any means, the disclosure strategy is the most advisable path. However, organizations using this strategy are at risk of having a Facebook page that looks deserted. The networks are very bare, with almost no followers. Even though using this strategy has some benefits, such as being able to claim your Facebook page and have some ‘strong’ links back to search engines, it can make an organization look disengaged with their followers.
Generally speaking, organizations do not use social media in its optimal form, seeing it more as another communication channel that is used to get the news ‘out there’. Within the networks, there is a lot to gain for organizations in respects to brokering information and positioning themselves in a more advantageous spot within the network. In order to achieve this however, these organizations need to start being more interactive with their followers. So in short, to be more successful on social media, organizations need to be more social.
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Appendix I: The separate Facebook pages analyses
In this appendix we present the network analysis of all the individual Facebook pages.
Abvakabo FNV (facebook.com/abvakaboFNV)
The image below shows you the network constructed from co-‐commenters (User who have commented on the same post) from the Facebook page of FNV Bondgenoten.
Abvakabo FNV has a Facebook page, which has 4,486 ‘fans’ (people who have liked the page). Making them the second largest Facebook page in our analysis. There are three obvious ‘communities’ within the Abvakabo FNV network. The red group, the green group and the smaller blue group. There have been three instances (posts) that have garnered serious attention within the network.
Within the network there are a number of large nodes. These nodes are more connected than other nodes. These people are Hans Derks, Nienke Eicker, Hans Gouw, Linda Mupte, and Hans Hassink. It is remarkable that searching for these people showed that they are
not in any way, other than possibly being a member of the Abvakabo FNV, involved with the union. They seemingly are people who are mentally very involved with the work the union does.
The largest node in the network is the Abvakabo FNV. This means that the administrator of the page is the most connected node in the network. This can be explained by multiple factors, such as the fact that the administrator has made the most posts and that the Facebook group of the Abvakabo FNV is also used as a sort of Webcare page. Users who have a complaint or question post to the Facebook page, and the Abvakabo FNV reacts.
Other key statistics about the network are as follows:
Key Indicator Value Explanation
Average degree 63,172 The average number of
edges a node has Avg. Weighted degree 68,621 The average number of
edges a node has, taking into account how
important those edges are
Graph density 0,117 The total number of edges
divided by the actual edges
Modularity 0,45 The degree to which the
network breaks down into sophisticated communities Avg. Path Length 2,204 The average distance of
nodes you need to travel from one node to another node
Nodes 541 Number of people who
have commented on a post
Edges 17088 The number of all
connections between all nodes
The degree shows that that nodes on average are connected to 63 other nodes, this means that posts on the page on average have 63 different people replying to them. The average weighted degree takes the number of edges but applies weight these edges, an edge to a more connected node is ‘heavier’ than a connection to a less connected edge. Because the average weighted degree is higher than the average degree, most people seem to be connected to nodes with a higher betweenness.
The graph density tells us that out of all possible edges, only 11,7% percent of the possible edges have manifested. The higher the graph density, the more connected all nodes are. The average path length shows us that if you want to go from one node to another, you need only to travel, on average, past only 2,204 other nodes.
Modularity is always a number between 0 and 1. The closer this number lies to one, the better the network can be broken down into autonomous communities. In total, there are 541 edges, which represent persons who have responded to a post on the Abvakabo FNV Facebook page.
There are 17088 edges, which represent a connection from a node to another node, or the fact these connected persons have commented on the same post
FNV Bondgenoten (facebook.com/FNVBondgenoten)
The image below shows you the network constructed from co-‐commenters (User who have commented on the same post) from the Facebook page of FNV Bondgenoten.
FNV Bondgenoten has a Facebook page, which has 7,824 ‘fans’ (people who have liked the page). Making them the largest Facebook page in our analysis. This is also very well reflected in the network, as it is much more cluttered than the other networks. There are several large nodes in the network and several communities that are also quite visible beyond their color.
the FNV Bongenoten use their Facebook page for both spreading information and
webcare, while they have got two Twitter accounts, a dedicated Webcare account and an informative account.
The other large nodes within the network are Wim van de Veen, Frans Willemen, Roelie Faber, Mila Blauwe Storm, Dieter Ernst Klobedanz, Erwin Papilaja and Peter Lugten. All these people are possibly members of the union, but that is where their official
involvement stops.
The fact that this is the largest Facebook group also shows in the number of
communities that are present. There are six clearly distinct communities that can be seen in their different colors.
Key Indicator Value Explanation
Average Degree 34,37 The average number of
edges a node has Avg. Weighted Degree 40,946 The average number of
edges a node has, taking into account how
important those edges are
Graph Density 0,058 The total number of edges
divided by the actual edges
Modularity 0,514 The degree to which the
network breaks down into sophisticated communities Avg. Path length 2,286 The average distance of
nodes you need to travel from one node to another node
Nodes 594 Number of people who
have commented on a post
Edges 10208 The number of all
connections between all nodes
The key statistics of the network of the FNV bondgenoten network are very similar to the statistics found of the Abvakabo FNV network. The average degree has fallen some, meaning the nodes are less connected that in the network of the Abvakabo FNV. This can also be seen in the Graph Density, which is 0,058. This is lower than the density of the abvakabo FNV Facebook page, and means that there are less possible edges in this network than in the network of the Abvakabo.
The modularity is around the same level, meaning that this network can be broken down fairly well into communities. The average path length is slightly higher, meaning that more nodes need to be travelled when travelling between two random notes. This is logical, because there are less possible edges (a lower graph density) so there a less ‘roads to travel’. The total number of nodes is 594, more than the Abvakabo, but there are fewer edges, namely 10208.
CNV (facebook.com/vakbond)
The image below shows you the network constructed from the Facebook page of CNV.
CNV has a Facebook page, which has 1,126 ‘fans’ (people who have liked the page). The first that jumps out when analyzing this network is that there are multiple disconnected groups from the main network. These are users that have commented on one single post, started by a single user (if the post was started by the CNV, they would be connected to the CNV Vakbond node).
Another remarkable feature is that all nodes are relatively small. Only two nodes are larger compared to the rest, the first is a man cold Jan Snippenberg. Jan is a brick mason, he says politics and the union are one of his hobby but is not connected to the CNV as a board member, advisor or any other professional role. The chance is highly likely that he is indeed a member of the union.
Other key statistics of the CNV are as follows.
Key Indicator Value Explanation
Average Degree 4,4 The average number of
edges a node has Avg. Weighted Degree 5,323 The average number of
edges a node has, taking into account how
important those edges are
Graph Density 0,069 The total number of edges
divided by the actual edges
Modularity 0,552 The degree to which the
network breaks down into sophisticated communities Avg. Path Length 2,099 The average distance of
nodes you need to travel from one node to another node
Nodes 63 Number of people who
have commented on a post
Edges 143 The number of all
connections between all nodes
The CNV Facebook page has a lot less fans than the other two networks, and this can also be seen in the statistics. The degree is 4,4 meaning that one person is only connected to 4,4 other people. A large drop-‐off from the other two networks.
The graph density is lower than that of the FNV Bondgenoten, but lower than that of the Abvakabo FNV. Of the possible edges in the network, 6,9% of the edges has manifested.
Modularity and average path length stays about the same, meaning all thee networks can be broken down into communities fairly well, and in all network one needs to travel slightly over 2 nodes when travelling from one node to another.
What stands out, is that of out of 1,126 fans, only 63 have commented on the posts (62, excluding the page administrator). This translates to 5,59%. In relation to the other pages, the FNV Bondgenoten has 7,59% of their fans respond and Abvakabo FNV has 12,05% of their fans respond, even though the other Facebook pages have much more likes, their response rate is also much higher. A look into the posted content could explain why this is.
Algemene Bond voor Uitzendondernemingen (facebook.com/deABUnl)
The Dutch ‘Algemene Bond voor Uitzendondernemingen’ shows us an abnormal picture. No colors, no shapes or sizes and most importantly, no edges. This is because there are almost no comments to be found on the Facebook page of the ABU. There are a relatively normal amount of postings, but there is a relative small numbers of likes: Only 134 people have liked the page.
Two commenters are employment agencies, and two other are persons. The fifth commenter on the posts is the ABU itself. However, there was never more than one comment on a post from september 1st 2013 to september 1st 2014.
This very small network of five loose nodes represents four people and the site administrator that have commented on a posting on the ABU page.
Posting further statistics is close to useless. Gephi refuses to give us anything coherent on such a small network were virtually nothing is worthy of analysis. Here are the further statistics Gephi has given us from this network.
Key Indicator Value Explanation
Average Degree 0 The average number of
edges a node has Avg. Weighted Degree 0 The average number of
edges a node has, taking into account how
important those edges are
Graph Density 0 The total number of edges
divided by the actual edges
Modularity 0 The degree to which the
network breaks down into sophisticated communities
Avg. Path Length 0 The average distance of
nodes you need to travel from one node to another node
Nodes 5 Number of people who
have commented on a post
Edges 0 The number of all
connections between all nodes
FNV Jong (facebook.com/fnvjong)
The FNV Jong Facebook page has 1546 likes and is aimed at youth moment of the FNV Bondgenoten union. They have a lot more likes that the CNV, but their network is not as interesting at first glance. There are a number of communities in the network, and also a lot of lone edges. This is the result of some people commenting on just one post
throughout the year.
What is remarkable is that FNV Jong cannot be found in the network. This makes it seem as if FNV Jong makes a post, only to not pay attention to what happens to it and if there are any further questions about it.
Other statistics about the FNV Jong network are as follows.
Key Indicator Value Explanation
Average Degree 1,935 The average number of
edges a node has Avg. Weighted Degree 1,935 The average number of
edges a node has, taking into account how
important those edges are
Graph Density 0,065 The total number of edges
divided by the actual edges
Modularity 0,687 The degree to which the
network breaks down into sophisticated communities
Avg. Path Length 1 The average distance of
nodes you need to travel from one node to another node
Nodes 31 Number of people who
have commented on a post
Edges 30 The number of all