#Liberty breach: An exploratory usage case of NodeXL Pro as a social media analytics tool for Twitter

Abstract: Social media analytics uses data mining tools, platforms, and analytics techniques to collect and analyse infinite amounts of social media data. Social media analytics tools extract patterns and connections from data, for insight into market sentiments and requirements, to enhance business intelligence. ‘Network Overview, Discovery and Exploration for Excel Pro’ (NodeXL Pro) is a social media analytics tool that simplifies basic network analysis tasks and supports the analysis of social media networks. NodeXL Pro does sophisticated ‘crawling’ (extracting data) across a range of social media platforms. Through a qualitative case study design, this study explores and describes the use of NodeXL Pro through empirical and multimodal analysis and social network visualisation of social media data of the Liberty Holdings Ltd data breach crisis case in June 2018. The hashtag ‘#Liberty breach’ resulted in 10 000 data sources (‘tweets’) from the social media platform Twitter. This study is unique on two levels. Firstly, it appears to be the first study in the South African marketing literature to use NodeXL Pro in social media analytics. Secondly, it presents the case study as a usage case to describe, in a step‐by‐step way, the functionalities of NodeXL Pro through social network analysis. The main finding of the paper focuses on the usability and manifold features (including the integrated visualisation tool) of NodeXL Pro. This social media analytics tool can open doors for marketing scholars and practitioners alike to measure, map, and model collections of connections

Digital Disruption beyond Uber and Airbnb – tracking the long tail of the sharing economy

The sharing economy can be regarded as a discontinuous innovation that creates increased abundance throughout society. Extant literature on the sharing economy has been predominantly concerned with Uber and Airbnb. As little is known about where the sharing economy is gaining momentum beyond transportation and accommodation, the purpose of this paper is to map in what sectors of the economy it is perceived to gain traction. Drawing on data from social and traditional media in Sweden, we identify a long tail of 17 sectors and 47 subsectors in which a total of 165 unique sharing-economy actors operate, including sectors such as on-demand services, fashion and clothing, and food delivery. Our findings therefore point at the expanding scope of the sharing economy and relatedly, we derive a set of implications for firms

Quality Indicators for Social Business Intelligence

Comunicació presentada a 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (Granada, Spain, 22-25 Oct. 2019)The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator

Examining Social Media Data Analytics and Decision-making in a South African Bank.

This paper investigates the interplay between social media data analytics (SMDA) and strategic decision-making in the banking industry, to understand how these factors impact sales lead generation. Using the General Systems Theory as an integrative framework, we analysed semi-structured interviews and conducted a thematic analysis to identify patterns in the data. The study identifies and explores the constraints between the socio and technical aspects of decision-making and how they affect sales lead generation in banks. We present five themes that emerged from our analysis, including social media data (SMD) perceptions, challenges of data analytics, social media use for lead generation, data availability and quality, and subsystems of decision-making. By synthesizing our findings, we provide a thematic model to provide insights into the relationship between SMDA, strategic decision-making, and sales lead generation in the banking industry

Digital entrepreneurship and field conditions for institutional change - Investigating the enabling role of cities

Digital entrepreneurship may result in institutional turbulence and new initiatives are frequently blocked by vested interest groups who posit superior financial and relational resources. In this paper, we explore the role of cities in facilitating digital entrepreneurship and overcoming institutional resistance to innovation. Drawing upon two historical case studies of digital entrepreneurship in the city of Stockholm along with an extensive material on the sharing economy in Sweden, our results suggest that cities offer an environment that is critical for digital entrepreneurship. The economic and technological diversity of a city may provide the field conditions required for institutional change to take place and to avoid regulatory capture

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems

Assessing value creation in digital innovation ecosystems: A Social Media Analytics approach

© 2018 Elsevier B.V. This paper explores the creation of value through the interactions of consumer and professional stakeholders in digital innovation ecosystems. We examine this by applying the methodological approach of Social Media Analytics (SMA) which is an interdisciplinary approach that seeks to combine, extend and adapt methods for analysing social media data. By utilising the SMA framework to track user-generated contents published on social media platforms, we assess how consumer and professional stakeholders associate value to Storytel, a new entrant in the Swedish publishing industry that is offering digital subscription service for streaming audiobooks. Drawing from a dataset of 2633 user-generated contents, our findings illustrate the value-creating practices in which stakeholders in Storytel's ecosystems associate value to Storytel's digital innovation. Our findings further highlight that the value-creating practices arising from the interactions of consumer and professional stakeholders in social media give rise to the hybridisation of value, where multiple values drawn from existing value categories become merged in the studied case. This study contributes to extant literature on management of innovation and information systems by (i) shedding light on how value is created by examining value-creating practices as a result of the interactions between stakeholders and (ii) examining the resulting merging of value categories within digital innovation ecosystems and thus exploring the hybridisation of value

How does social media analytics create value?

The surge of interest in big social data has led to growing demand for social media analytics (SMA). Having robust SMA can help firms create value and achieve competitive advantages. However, most firms do not always know how to embrace big social data to establish a path to value. This study addresses this key question to deepen our understanding of how different types of SMA can be applied to create value. Specifically, the findings show the significant uses of opinion mining or sentiment analysis, topic modeling, engagement analysis, predictive analysis, social network analysis, and trend analysis. Finally, the study provides directions for the challenges and opportunities of SMA to maximize value