An Evaluation of Performance and Competition in Customer Services on Twitter

Abstract

This is the final published version of the article (version of record). It first appeared online via ACM at https://dl.acm.org/citation.cfm?id=3191631 . Please refer to any applicable terms of use of the publisher. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. ABSTRACTWith an increasing number of consumers using social media platforms to share both their satisfaction and displeasure about the products and services they use every day, organisations with a customer service focus are recognising the importance of rapidand genuine -online engagement with their customers. In turn, consumers increasingly judge organisations on the quality of customer service and degree of responsiveness to online queries. This paper presents an extensible framework for evaluating direct engagements of customer service teams with customers on Twitter. Furthermore, this framework provides the capability to measure and analyse indirect engagement with industry sector rivals, especially their patterns, frequency and intensity. By applying graph analysis to these Twitter interactions, our framework generates various analytical measures and visual representations, exemplified through a case study based on seven major UK telecoms companies. With a dataset consisting of 15,000 tweets and 3,500 user profiles, the results provide sustained evidence for indirect engagements between business rivals, with customer queries acting as a trigger for intense competition between companies based in the same industry sub-domain.

Modelling and Analysing Behaviours and Emotions via Complex User Interactions

“…Mairesse (2007) [196], research study, presented a model to extract the personality and posted the source code publicly to be used. 6 . Suggested model built based on querying the Medical Research Council (MRC) Psycholinguistic Database 7 and LIWC, also, the Mairesse's tool were validated and assessed [354,255,320].…”

“…New and larger datasets: The recent events occurs regarding Facebook, suggested to explore other social networks (e.g. Twitter, LinkedIn) to extract a new dataset, number of companies are using Twitter as a main context of the communication between their customers and commonly used to report technical issues, Twitter offers a rich API [198] makes it a productive environment for researchers to collect and analyse large-scale longitudinal datasets [5,6,4]. Furthermore, since Twitter is largely an open, public platform the data can be used in investigating further and verifying the model outcome and improving the accuracy of the model.…”

“…These multiple interactions can be measured, profiled and modelled to critically analyse the communications and related processes and provide valuable insight into improving system architectures and designs. Furthermore, it provides a predictive capability to better understand digital behaviours -particularly through big social media datasets and corpora [186,271,220,45,5,6,4] -and thus develop systems that are more resilient and robust against undesirable behaviour and security breaches, such as "insider threat" scenarios [168,236,331], cyberhate [46,352], as well as more generally for crime informatics [238,47,237,239]. [143,266,112,159,51]) This research project is grounded in the area of human-computer interaction where it intercepts with the fields of artificial emotional intelligence and behavioural sciences under the umbrella of computer science (see Figure 1.1).…”

Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study

“…Apart from the "official" trend lists provided by the platform (on the website or through API endpoints), generating insight from trends and topics detection has been receiving increasing attention from across a variety of big social data-driven research domains, with varying results; in health for example, monitoring and analysis of trending topics on social media has been adopted to measure emerging public health issues, such as the spread of influenza [1,2]. Furthermore, across marketing and business domains, topic detection and classification are valuable approaches in extracting knowledge and insight on public opinions from posts on social media [3][4][5], including analysing voting intentions and political view of users [6]. With the increasing popularity and use of social networks across a wide range of domains, the impact of trends on public opinion and perceptions has transformed social media campaigns and public relations strategy.…”

“…https://developer.twitter.com/en/docs/trends/trends-for-location/apireference/get-trends-place5 Isolated trends are those that have trended in one place, i.e. their indegree equal 1.…”