Understanding the true nature of the Gephi Chart

The year 2020 has commenced in the most unexpected direction. With a highly contagious virus spreading rapidly through humanity, many party goers are opting to stay home now more than ever. This paves the gold opportunity for live stream videos to capture larger audiences. However, in order to achieve this using the video analytics collected, The Gephi Chart becomes highly effective. A simple definition of the chart is given by authors Heymann. S & Grand. B.L (2013) who both conclude that Gephi is a generic visualization software tool, that analyzes data input while providing a complex imagery of various  social clusters and how they interact as the output.

Firstly it is essential to deeply grasp the capabilities and magnitude of the software. According to Grandjean. M (2015) a Gephi specialist who supports this way of thinking regards social networks as groups consisting of two components, firstly the actors and secondly the interactions that take place. To dive deeper into understanding the elements of the model Akhtar, Javed & Sengar (2013), go forth into establishing an important distinction within Social Network Analysis (SNA). They states that, SNA is seperated into two perspectives, one being the ego network analysis and the other being complete network analysis. The generic nature of Gephi allows for social media platforms such as Facebook; whereby majority of live videos are streamed on, to be visually examined using node-link diagrams. Node-link diagrams consist of data maps of theorized information, whereby a visual language is created through the use of color and size. The objectives behind this picturial phenomenon are:

1. The chart enables the analyst to edit and filter in or out various variables that highlight areas of strength or weaknesses.
2. Gephi, allows for information to be used to project the results of possible changes if made.
3. Visualization tools such as Gephi, allow for companies to view their data from a new perspective.

Here is a video link on the fundamentals of Gephi

 

The missing links within understanding live stream video social networks

When video streaming is introduced to Gephi, then it is essential to know what is lacking within the knowledge understanding of the various video networks and how live stream companies can effectively utilize the model to decrease knowledge gaps. As previously mentioned, social networks (SN) consist of two methods of evaluating statistics. Firstly, ego network analysis (ENA) is characterised as singular (i.e a person) and focuses on the dominant factors that influence interactions amoung SNs (Smith. J.A, 2019 and Akhtar, Javed & Sengar, 2013 and De Salve. A., et al, 2019). These factors can be viewed as behavioural analytics such as comments, likes, dislikes, hearts and etc. The second method of data interpretation is complete network analysis (CNA). This form of information investigation takes into account geographical locations, user distancing, and effect of distance. In streaming analytics these classifications are found in the video performance analytics, whereby the page owner can select whether they want to see countries where the video was viewed and how many minutes in percentage each place contributed during the live.

When examining video data both, perspectives of SNA are significantly considered to better understand one’s network. These analytics are highly general in how audience engagement and interactions are interpreted. The Gephi chart offers live stream platforms and companies the lack of knowledge bridging between:

1. The creation of deeper social networks (SN) based on the different user locations that like, heart, share or comment on a video.
2. Understanding the degree of influence or impact various countries have on one another during a live stream or after when live video KPIs are met.
3. Determining who the major ego network drivers are & how to utilize these individuals or organizations, to reach far networks focusing on distance to create a greater effect.

Please note: Facebook has discontinued  Netlytic API access

Table 1:

Effective implementation of the Gephi Chart to visualize the social networks of live video data

The Gephi chart and Facebook analytics work very well together to  provide a significant picture. When the features and specifications are applied correctly then a visual graph such as the one on the right is created.

 

Image 1

Furthermore, when implementing live video results generated, establishing key plugins is crucial towards the final visual graph version. Plugins can be viewed as the various filters that input a certain format into how the data is manipulated visually, for example choosing to view country maps for the noting of SN locations (see image 2). These plugins are the dimensions of the overall graph, as each plugin targets a specific category within the appearnce and layout.

 

Image 2

 

How Gephi deepens digital marketing capabilities for live stream videography

Gephi offers deep learning capabilities through the ability of calculating the average (avg) degree of influence between nodes, the avg concentration of bridges between links or size of the network. When geography is connected to the chart, then marketing learning capabilities of the live stream companies are viewed in greater depth. For example, the avg for bridging centrality between SNs and their geographical locations shows the avg distance a live stream network has to it’s external environment. Another illustraction could be measuring the rate of influence one SN has on another against the geo-location to discover the speed at which the live stream is organically circulating once it reaches a SN. There is a plethora of digital marketing tools for live streams yet, are highly limited in their functions and outlook on data. However, Gephi offers exponential solutions to questions and needs that data analysts have or might have in the future.

 

 

 

 

 

Image 4

 

 

References

Akhtar, Nadeem & Javed, Hira & Sengar, Geetanjali. (2013). Analysis of Facebook Social Network. 451-454. 10.1109/CICN.2013.99.

Arnaboldi, V., Conti, M., Passarella, A. and Pezzoni, F., 2012, September. Analysis of ego network structure in online social networks. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing (pp. 31-40). IEEE.

De Salve, A., Mori, P., Guidi, B. and Ricci, L., 2019. An Analysis of the Internal Organization of Facebook Groups. IEEE Transactions on Computational Social Systems, 6(6), pp.1245-1256.

Facebook (2019) Radio Nachtlab videos [Online] www.facebook.com/radionachtlab/videos/ [Accessed 2 March 2020]

Grandjean, M., 2015. GEPHI: Introduction to Network Analysis and Visualisation.

Gruzd, A., 2020. Netlytic: Facebook is discontinuing API access their Page Public Content [Online] https://netlytic.org/home/?p=11671 [Accessed 27 February 2020]

Gephi (2017) The Open Graph Viz Platform [Online] https://gephi.org [Accessed 1 March 2020]

Heymann, S. and Le Grand, B., 2013, July. Visual analysis of complex networks for business intelligence with gephi. In 2013 17th International Conference on Information Visualisation (pp. 307-312). IEEE.

Smith, J.A., 2019. The Continued Relevance of Ego Network Data.

Youtube (2020) How to download a copy of Facebook Page data info [Online] https://www.youtube.com/watch?v=LIwkkM1TA-4 [Accessed 26 February 2020]

 

How to effectively analyse the digital marketing strategy for live-stream events using Burt’s Structural holes theory.

The main objective behind live streaming platforms.

In the recent years since the birth of the Boiler Room (Bellville. B, 2010) platform, live streaming on Facebook has increased rapidly. This disruptive innovative method of broadcasting DJ sets to viewers from the comfort of their home, created a modern intimate continuous supply for DJs to share their music all throughout the year. The objective behind streaming DJ sets to an online mass, is that the organization is able to maintain both a physical audience and capture an online audience to boost sales. The authors Schowalter. K & Srivastava. S.B (2019), conceptualize this form of interaction as the phenomenon called “structural bridging”. In the streaming industry this is the ability to capture disconnected individuals who do not attend gatherings as often. Furthermore, these types of organizations continue to create the demand for DJs when festival season is over. Live stream platforms, enable DJs to continue to push their music while raising their number on websites such as Beatport (Tempel. J, 2004), Spotify (Ek. D, 2006) and Resident Advisor (2001). 

 

A contemporary modern take on live broadcasting can be seen with the Cercle (Barbolla. D, 2016) concept. Barbolla’s event company, connects cultural heritage sites or unique locations with music through Facebook live streaming. The competitive advantage of the value proposition put forth by Cercle of being part of a story told in a historical setting, creates a high demand and engagement through digital marketing, to capture physical customers that attend the event. 

 

Here is a quick read on the growth and impact of Facebook Live videos.

 

The sphere of controls applicable in the live event industry.

Unlike festival or club events, live stream gatherings have a specific degree of what they can control. According to Dr Kar (Kar. A.K, 2015), social media controls are measured through key performance indicators that can be Facebook video analytics such as:

• Bounce Rate
• Price per click (PPC) or Click per pay (CPP)
• Traffic Volume 
• Audience engagement
• Physical attendees in the live stream

 

These KPIs, enable for the creation of value the page has on the current online audience. They give an overview of how much reach the facebook page is capturing and how well the live stream is doing during and after it has been published. The given analytics provide information on whether the choices made for digital marketing are effective and attractive towards the online customers. (See Image 1, and Image 2)

Radio Nachtlab video analytics

Image 1                                             Image 2

     

Facebook.com, 2019

Understanding Burt’s Structural holes theory in relation to the online audience.

 

Ronald Burt (1986), describes social media networks as a complex connection of groups made up of various individuals that intertwine and interact with one. These network groups can be separated by culture, personality, geographical location, technology preferences and much more. The major objective behind Burt’s theory, is this ideology of forming links between clusters through social distance (See diagram 1).

  Diagram 1

Diagram 1 represents two simple clusters that are linked by the basic interactions and engagements between each other. They can be characterized as facebook groups or the various KPIs used to measure performance. A crucial element to highlight is the interconnection that links the two clusters together; this is called bridging and the sweet spot for live stream platform marketing. The main aspiration in live streaming is, bringing about the intercultural effect of joining different worlds together through watching the same video at the same time. When this concept of bridging is accomplished then the brand increases it’s digital presence through an increase between user profile geo-distancing. However, in order to understand the strategy behind the structural holes theory, KPIs must be placed within the group to grasp the cause and effect each one has.  

 

Here is a 2 min video of Burt. R (2015) explaining the importance of networks and brokers.

 

Implementing Burt’s theory to critically analyze  live video analytics to measure set KPI’s. 

Diagram 2The fastest method of fully understanding Burt’s theory is by implementing the concept in a practical sense in diagram 2. Looking at the video analytics images provided above taken from Radio Nachtlab, BR can be seen as the Clicks to play results, while PPC can be viewed as the Post clicks. Moreover, TV would be portrayed as video views 

 

What this drawing illustrates to the organization about the video is that, if the reach for the target audience is there, plus the reactions given, including the BR & PPC equals the bridge between the gap of actual online customers to real ones. When the link is formed from the obtained data analytics accumulated from the live video, illustrates a geographical graph. (See image below)

Image 3

Facebook.com, 2019

 

What Burt’s theory affirms about the marketing strategy used in live platforms. 

Image 3, shows the diverse global magnitude and degree of reach a live stream has when KPIs are met. If Burt’s theory is implemented correctly; to know which controls interact withone another well to increase the online audience then, calculating the distance between viewers becomes more feasable.

By considering the distance in the live event industry, organizations are able to comprehend and emphasize on what is creating the most engagement, what is attracting foreign listeners or viewers, what is cultural context and finally what is enabling the conversion between digital and physical event goers through distance analysis.            

References

Boiler Room, (2010) About Us [Online] www.boilerroom.tv [Accessed 4 February 2020]

Facebook, (2019) Radio Nachtlab videos [Online] www.facebook.com/radionachtlab/videos/ [Accessed 1 February 2020]

Faust, K. and Romney, A.K., 1985. Does structure find structure?: A critique of Burt’s use of distance as a measure of structural equivalence. Social Networks, 7(1), pp.77-103.

Kar, A.K., 2015. Applications of analytics in social media. In Foundation Innovation Tech. Transfer Forum Newsletters (IIT Delhi) (Vol. 21, No. 1, pp. 6-8).

Schowalter, K., Goldberg, A. and Srivastava, S.B., 2019. Bridging Perspectives on Bridging: A Framework of Social Contexts that Integrates Structural and Cultural Bridging.

Yoshida, A., Higurashi, T., Maruishi, M., Tateiwa, N., Hata, N., Tanaka, A., Wakamatsu, T., Nagamatsu, K., Tajima, A. and Fujisawa, K., 2019. New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests. Data Science and Engineering, pp.1-17.