  We present some initial results from a case study in social media data
harvesting and visualization utilizing the tools and analytical features of
NodeXL applied to a degree asymmetric vertex graph set. We consider twitter
graphs harvested for topics related to suicidal ideation, suicide attempts,
self-harm and bullycide. While the twitter-sphere only captures a small and age
biased sample of communications it is a readily available public database for a
wealth of rich topics yielding a large sample set. All these topics gave rise
to highly asymmetric vertex degree graphs and all shared the same general
topological features. We find a strong preference for in degree vertex
information transfer with a 4:25 out degree to in degree vertex ratio with a
power law distribution. Overall there is a low global clustering coefficient
average of 0.038 and a graph clustering density of 0.00034 for
Clauset-Newman-Moore grouping with a maximum geodesic distance of 6.
Eigenvector centrality does not give any large central impact vertices and
betweenness centrality shows many bridging vertices indicating a sparse
community structure. Parts of speech sentiment scores show a strong asymmetry
of predominant negative scores for almost all word and word pairs with salience
greater than one. We used an Hoaxy analysis to check for deliberate
misinformation on these topics by a Twitter-Bot.
