Right Relevance (RR) provides curated information and intelligence on ~50 thousand topics. This includes:
- Topic relationships including related topics & semantic information like synonyms.
- Topical influencers (~2.5M) with score and rank.
- Topical content and information in the form of articles, videos and conversations.
Additionally, Right Relevance provides an Insights offering that combines the above Topics and Influencers information with real time conversations to provide actionable intelligence with visualizations to enable decision making. The Insights service is applicable to events like elections, brands, emerging technologies, issues/activism, conferences, product launches etc.
This report is a summary of graph analysis of engagements and conversations including retweets, mentions and replies of tweets related to Devops.
The report leverages tweets sampled from May 1st to May 31st 2017 and along with Right Relevance topics, topical communities’ and articles data form the basis for the analysis.
Note: The anlaysis is based on EN-only (English) data.
The phrases used for gathering tweets are: “devops”
Most of the summary report is extracted from the analysis collateral in the form of:
- Tableau Online Dashboard: Visualizes graph analysis results via charts and tables. Insights include flocks, top trending terms, top hashtags, top Users/accounts, RR topics, top tweets and several other measures. Faceting is supported per flock, RR topic and Twitter/RR account.
- Gephi Communities Graph Visual: Extracts are shown below.
The analysis methodology is outlined at http://220.127.116.11/insights
Devops Communities Graph
Community detection graph algorithms like Walktrap and InfoMap are used to identify communities (as sub-graphs) in our engagements graph built using Neo4j & R. Graph visualizations are done via Gephi.
The all engagements graph, which includes mentions, is dense with one large engaged subgraph/community and set of smaller but active communities. The communities will be outlined in the “interesting flocks” section.
For Zoomable clickable link here.
The all engagements graph shows a large highly engaged community (green) towards the bottom left. This is the most active flock, led by CloudExpo (@CloudExpo) and it’s related accounts like ThingsExpo along with DevOps Summit (@DevOpsSummit), CRM_CWS_Cloud (@CRM_CWS_Cloud), IBM Dev Ops (@IBMDevOps), CA Technologies (@CAinc), Aruna Ravichandran (@aruna13), Nutanix Inc (@nutanix) etc.
Latent Dirichlet allocation (LDA) based text analysis of the tweets is used for identifying high value trending terms. These along with hashtags and Right Relevance topics form the basis for identifying top conversation themes during the analysis timeframe.
Fig 2 shows the top trending terms, hashtags, RR topics and account locations distribution for conversations around ‘devops’ during the timeframe (may 2017) monitored.
The top trending terms, hashtags and RR topics bring out the following as the</stro top themes in May 2017.
- The 20th Cloud Expo and 7th Devops Summit in NYC from June 6-8 shows the highest engagements with several high value accounts involved.
- Devops Enterprise Summit in London (June 25-26).
- Devopsdays Austin (May 4-5).
- AI and Big Data are becoming big areas of activity around Devops.
- Atlassian seems to have built an active community and conversations.
- Red Hat with Openshift and Ansible.
- Azure and AWS with have their own active communities.
- IBM (w/Compuware driving) esp. focused on mainframe devops.
The location distribution of the users/accounts driving conversations around ‘devops’ isn’t very surprising. London has bubbled close to the top due to the DevOps Enterprise Summit chatter.
The top “Devops” tweet (Fig 3) during this timeframe, by a long way, was by Lemi Orhan Ergin (@lemiorhan).
Accounts via RR Topic Faceting
Using Right Relevance topics as facets (via the Insights Tableau dashboard) is a great way to pinpoint top accounts connected to a related theme within the braoder context.
The top influencer accounts for ‘openstack’ and ‘it service management’ within the context of ‘devops’ conversations and engagementsare outlined below.
The top 5 ‘Devops – Openstack’ conversation related accounts are CloudExpo (@CloudExpo), Chef (@chef), Ansible by Red Hat (@ansible), Red Hat OpenShift (@openshift), and Andrew Clay Shafer (@littleidea).
The top ‘Devops – IT Service Management’ conversation related accounts are CloudExpo (@CloudExpo), Gene Kim (@RealGeneKim), Atlassian (@Atlassian), DevOps.com (@devopsdotcom), InformationWeek (@InformationWeek) and CA Technologies (@CAinc).
More themes will be explored as part of the flock analysis later in the report.
Topical Influence: Tribes
Measuring influence is not deterministic. It’s a subjective task with numerous different methodologies and is generally relatively dynamic and ephemeral in nature. Right Relevance platform measures users/accounts influence in 2 distinct ways: topical & engagement-based.
Right Relevance algorithmically mines web content and social media at scale to determine topics and influencers and produce a measure of influence per topic termed as ‘topical influence’. Unstructured text, network connections, social signals along with semantic data, ML, NLP are leveraged to produce two sets of information; a set of ‘structured topics’ (~50K) with semantic information and; a connected graph of scored ranked influencers for each of these structured topics we call ‘topical influencers’ or Tribes.
Fig 6 also provides a list of the top 10 Right Relevance ‘devops’ influencers along with the top 10 domains where influencers post about ‘devops’. GitHub (@github), Docker (@Docker), Werner Vogels (@Werner), Martin Fowler (@martinfowler) and Adrian Cockcraft (@adrianco) form the top 5 “Tribe” influencers.
Right Relevance ‘engagement influence’ measures are calculated by applying a set of graph analysis algorithms, including PageRank and Betweenness Centrality.
The quality and quantity of engagements (RTs, mentions, replies), reach of tweets etc. are measured within the context of a subject (event, trend etc.). to measure Flock influence. The meaning of rankings within this methodology are documented at Twitter Conversation Performance Measures.
The first two lists (fig 7) are of the top 30 accounts by PageRank & Overall measures.
Overall rank is a normalized rank to reduce the skew towards users with large numbers of followers or a single tweet having a large number of engagements/RTs (often referred to as becoming ‘viral’).
PageRank brings up CloudExpo (@CloudExpo), DevOps Summit (@DevOpsSummit), Gene Kim (@RealGeneKim), nixCraft (@nixcraft), botchagalupe (@botchagalupe), DevOps.com (@devopsdotcom), DOES17 London (@DOES_EUR), Atlassian (@Atlassian) and Lemi Orhan Ergin (@lemiorhan) as the top 10 most impactful in May’17.
The top Overall measure, in spite of the normalized nature, doesn’t bring up many new interesting accounts to the top. Devops Top News (@DevopsTopNews) is the only new account that breaks into the top 10.
The results above lead to other measures becoming important to measure influence as discussed below.
The ‘Top Connectors’ list (fig 8) shows the top 30 accounts based on the ‘Betweenness Centrality’ measure.
Betweenness centrality, which is a measure of the degree to which a node forms a bridge or critical link between all other users. We use this as a measure of influence wrt value in being information and/or communication hubs.
The top accounts likeDevops Top News (@DevopsTopNews), DevOps Summit (@DevOpsSummit), CloudExpo (@CloudExpo), XebiaLabs (@xebialabs), DOES17 London (@DOES_EUR), Chef (@chef), Gene Kim (@RealGeneKim), Andi Mann (@AndiMann), F5 Networks (@F5Networks) and Ansible by Red Hat (@ansible) include several conferences, strategists, analysts, PR, advisors, publishers, personal branding experts etc. who have built up influence and value as news and information hubs in the ‘devops’ domain.
The value of this measure lies in that it bubbles up accounts with potentially real influence in terms of news and information dissemination on a given subject.
The engagements or “flocking” in the context of a subject (topic, event etc.) can lead to building of temporal communities with local influence that is not obvious by the standalone influence of the individuals or without the context of the event. The subgraphs aka communities formed by applying community detection graph algorithms are termed as ‘Flocks’.
As seen in the all engagements graph in Fig 1, there is one large engaged community with few smaller scattered communities. Flocks generally align well with the subgraphs aka communities noted in the graph.
Note: Flocks are named after the account with the highest PageRank in the flock.
Some interesting flocks are outlined below.
As seen from trending terms and hashtags (fig 9), mainframe devops seems like the primary driving theme behind the ‘IBMDevOps’ flock.
The top tweets for this flock (fig 10) show Compuware (@compuware) driving a majority of the conversations.
The top users for this flock are IBMDevOps (@IBMDevOps), Compuware (@compuware), Rob England (@theitskeptic), Rosalind Radcliffe (@RosalindRad) and Skyptap (@RosalindRad) form the top 5 users of this flock.
Fig 12 shows the location spread of the ‘IBMDevOps’ users/accounts.
‘IBMDevOps’ flock forms a clear community in the all engagements graph as visualized in the snapshot below (fig 13)
Note: There are several active flocks that are outside the scope of this report. Please contact firstname.lastname@example.org for details.
- Conferences and events like the 20th Cloud Expo and 7th Devops Summit in NYC (June 6-8), Devops Enterprise Summit in London (June 25-26) and Devopsdays Austin (May 4-5) generated the highest engagements with cloudexpo, containersexpo, devopssummit, does17 and devopsdays trending.
- Interactions between Devops and emerging areas like AI, IoT along with managing Big Data seems to be ever increasing.
- Red Hat with Openshift and Ansible seems to be doing very well with 3 accounts: Ansible by Red Hat (@ansible), Red Hat Openshift (@openshift) and Red Hat, Inc regularly among the top accounts in May ‘devops’ conversations.
- Atlassian has built influence along with an active community and conversations.
- AWS and Azure etc. have small but active communities. They don’t seem to have the same impact in ‘devops’ conversations compared to several other smaller players.
- IBM has built influence with Compuware driving conversations esp. focused on mainframe devops.
- Docker with containers, Chef, Puppet, Kubernetes etc. continue to driving conversations on automated deployment and management at scale in data centers.