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Engagement in Motion: Exploring Short Term Dynamics in Page-level Social Media Metrics

Benjamin Lucas1,2, Ahmed Shamsul Arefin1,3, Natalie de Vries1,3, Regina Berretta1,3, Jamie Carlson1,2, Pablo Moscato1,3

1 The University of Newcastle, Australia2 Newcastle Business School, Faculty of Business and Law3 The Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine

Presented by Ahmed Shamsul Arefin1,3, PhD

Presentation Outline

• Introduction • Research Problem• Methodology• Results• Significance of the work and future research directions• Questions

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Introduction

New social media and mobile services based communications have taken front and centre stage in scholarly business research in recent years (Rust and Huang, 2014)

These technologies provide new sources of customer data that managers can incorporate into marketing decision making (Peters et al, 2013; Rust and Huang, 2014)

Researchers in marketing data analytics are directing attention toward social media brand pages, user engagement and behavioural metrics (Jahn and Kunz, 2012; Naylor et al, 2012; de Vries and Carlson, 2014; de Vries, Carlson and Moscato, 2014)

Metrics at both page-level (PaLM) and post level (PoLM)

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Introduction

Social media metrics and their movements over time serve as indicators of how a brand is being received and perceived by the marketplace

Facebook now allows business account holders to compare their metrics against selected competitor’s pages

‘Social media analytics’ service providers offer platforms that deliver insights based on these metrics

5Facebook dashboard

An Example from FacebookM

y pa

ge

6Performance comparison of a Facebook page

An Example from Facebook

My Facebook Page

7Facebook page ID

An Example from Facebook

My Facebook Page ID

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Socialbakers.com : ‘analytics’ for Social Media Measurement

Socialbakers: ‘analytics’ tool

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Research ProblemPersonality Trait Profile Feature Pearson’s Correlation

Openness Like 0.102

Status 0.062

Group 0.077

Agreeableness Like -0.036

Neuroticism Like 0.075

Friends -0.059

Extraversion Like 0.034

Status 0.117

… … …

Source: Personality and Patterns of Facebook Usage, Bachrach et at. 2012

Even less research has sought to maximize value extraction from page-level metrics as opposed to post-level metrics on pages

Limited research has examined how social media metrics collected by brands can be meaningfully compared based on how they change over time in the real world

Research Example

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Research Questions

RQ1: Can PaLM dynamics be processed, analysed and visualised to create useful information and value for managers?

RQ2: What can PaLM dynamics tell us about the social media performance of known and unknown competitors and other businesses within and outside service business categories?

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Data Collection

Generation of the 15,625 Facebook pages PaLM data

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Facebook PaLM data

The Facebook PaLM data for randomly selected 15,625 Facebook pages from the top 100 000 brand pages.

* Data was collected at 17:00 (AEDT) on the 1st, 8th, 15th, 22nd and 29th of March, 2014 (i.e. for five consecutive weeks).

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Computation of MST-kNN and Graph Compute Distance matrices using correlation and distance metrics (Spearman’s, Pearson’s,

Robust, Cosine and Euclidean).

Input distance matrix and compute two proximity graphs: a minimum spanning tree and a k-nearest neighbour graph

The nodes (vertices) represent the brand pages

These vertices are connected by edges if they are connected:

In the Minimum Spanning Tree and They are found to be nearest neighbours according to the kNN algorithm

We compute the kNN cliques for ‘k=3 to 6’ and merge the outcomes with the MST-kNN’s.

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Facebook PaLM data

The proposed method for finding the best grouping of the Facebook pages using ‘check-ins’, ‘likes’ and ‘talking about this’ (PaLM).

The Proposed Method forExploring the PaLM data

Previous applications of the MST-kNN method

• U.S. Stock market time series data (Inostroza-Ponta, Berretta, & Moscato, 2011)

• Yeast gene expression data (Inostroza-Ponta, Mendes, Berretta, & Moscato, 2007)

• Alzheimer’s disease data - in the order of 1 million data elements (Arefin, Mathieson, Johnstone, Berretta, & Moscato, 2012)

• Prostate cancer data (Capp et al., 2009)

• Shakespearean era text corpus (Arefin et al. 2014)

These examples show the methodology proposed here has a proven scalability for larger datasets

20 graphs=

Distance Computation and Variations in k

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Distance Metrics:• Pearson• Spearman• Robust• Euclidean• Cosine

Values of k for kNN Cliques: k=3 k=4 k=5 k=6

• Applied a ‘random permutation test’ to find the best partitioning

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- 15625 Nodes (Facebook pages)- 18516 Edges

- 286 Clusters- Cosine distance- k=3

The MST-kNN with kNN Cliques

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New York City

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Memetic Ordering of the Clusters and Page Categories

Memetic gene ordering method introduced in Moscato et al (2007)

Investigate relationships amongst categories

‘Clusters of clusters’

Demonstrates the affinity of the clusters when they mostly contain similar categories

Brand categories with a similar number of pages in clusters were put together

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An Example of Memetic Ordering - Moscato et al (2007)

The original image (960 x 960 px) Random permutation of rows and columns

Hierarchical clustering- TIGR ‘Viewer’ Memetic orderingImage source: ACB Report 2009

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Facebook Page Categories

Distribution of Facebook brand page categories (477) in 286 clusters

21Memetic ordering of Facebook brand page categories (477)

Memetic Ordering

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Graphs and Results (cibm.newcastle.edu.au)

Supporting materials for further research

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Discussion

The result of the MST-kNN+ kNN cliques method demonstrates potential for marketers working with offline ‘real-world’ service businesses

Enables them to compare social media performance with other brands, derived from the natural structure of data from any given timeframe and geographic location, comprising known competitors and unknown competitors

Practical applications:• Hospitality Industry• Health Services• Universities• Tourism body, Local councils• Airport• Shopping Mall, and so on..

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Future Research

Analyses for over a 12-month (or longer) time period to account for trends and cycles

Researchers could examine movements in social media data over any period of interest (e.g. weekend, holiday season, months of the year)

Real-time capabilities and GUI development

‘Dig-deeper’ capabilities other social media metrics and data sources (PaLM and / or PoLM)

Developing business models around new data formats and analysis techniques in the social media context for application in practice (e.g. see: Hartmann et al, 2014)

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Acknowledgements

The authors would like to thank Socialbakers (www.socialbakers.com) for their assistance with this study

The authors would like to thank Shannon Hochkins for his assistance with this study

Thanks to Mario Inostroza-Ponta for the MST-kNN example images.

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Questions + Comments?