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Social Media Analytics for Better Understanding of the Digital Gig Economy

27 April 2018 1 comment

Owing to the proliferation of digital platforms facilitating online freelance work such as Upwork, Fiverr and Amazon Mechanical Turk, the number of digital gig workers has been continuously increasing worldwide. In 2015, there were as many as 48 million digital gig workers [1]; between 2016 and 2017, a 25% increase in the number of such workers was reported [2].

Digital gig work is indeed attractive to many, with a number of benefits that such independent workers are perceived to enjoy, e.g., flexible working hours, reduced transportation costs, wide range of projects to choose from. However, there exist potentially distressing issues, e.g., lack of job security, tough competition, substandard wages, which are especially pronounced in developing country settings [3]. Whereas traditional media such as news were unable to pinpoint or bring attention to these concerns, social media analysis–done manually by Cision in 2017–provided a window to the thoughts of independent workers which led to the fine-grained identification of issues that they are faced with [4].

As part of the currently ongoing Social Media Analytics Research and Teaching @ Manchester (SMART@Manchester) project funded by the University of Manchester Research Institute (UMRI), we aim to automatically gain insight into people’s perceptions of digital gig work, based on their posts on social media platforms such as Twitter and Facebook, as well as on review sites such as Glassdoor.

Specifically, we wish to test the currently prevailing assumption that digital gig work is experienced differently in the Global South compared to the Global North. Workers tend to make comparisons with their local benchmarks (i.e., office-based work), and it is believed possible that in the Global North, digital gig work is worse than prevailing benchmarks, whereas in the Global South it is better.

The following are some of the research questions that will be addressed as part of this case study.

  1. How do digital gig workers feel about their jobs?
  2. Which topics pertaining to decent work standards do they frequently talk about?
  3. Are there any differences—in terms of sentiments and topics—across different geographic locations, or across genders?

The first question can be answered by opinion mining while the second is addressable by topic identification. To determine whether there are differences with respect to opinions and topics, between the Global North and South or between genders, results from opinion mining and topic identification need to be combined with social media content metadata (e.g., geographic locations). 

In the way of opinion mining, we are currently investigating the use of an automatic emotion identification tool called Illuemotion which was developed by University of Manchester final-year Computer Science student, Elitsa Dimova. The web-based tool, a screenshot of which is provided below, is underpinned by a neural network model that analyses tweets to determine the most dominant emotions expressed, which can be any of anger, fear, joy, love, sadness, surprise and thankfulness.

The image below shows one of the tweets directly fetched by the tool from Twitter (via their API) when supplied with “#upwork” as input query. The tweet, which speaks of hidden dangers of being a digital gig worker, was detected by Illuemotion as expressing sadness and fear. One of our next steps is to apply the tool on a collection of thousands of tweets to allow us to analyse them across different geographic regions as well as genders.

As we are analysing data that pertains to human emotion, ethical considerations are being taken into account, especially bearing in mind that we also do not wish to compromise any of the digital gig workers who are social media users. For example, many Twitter users are unaware that what they post publicly can be used to identify or (reverse) look them up. They also have a right to be forgotten (i.e., they can delete their posts as well as their accounts). Overall what this means for us researchers who make use of their data is that in scholarly publications, we should provide only aggregated results and ensure that we do not include any identifiable information. These and other ethical considerations were discussed in detail in the recently concluded symposium in the Academy of Management Specialised Conference on Big Data entitled, “Ethical and Methodological Considerations for Management Research in the Digital Economy” held at the University of Surrey from the 18-20th April.

As well as two other SMART@Manchester case studies, the above described research questions on perceptions of digital gig work and our proposed approaches will be presented in the upcoming 4th International Workshop on Social Media World Sensors (Sideways 2018) co-located with the 15th European Semantic Web Conference to be held in Heraklion, Crete, Greece from the 3rd-7th June.

References:

[1] Kuek, S.C. et al. (2015) The Global Opportunity in Online Outsourcing. World Bank, Washington, DC. Available at: http://documents.worldbank.org/curated/en/138371468000900555/The-global-opportunity-in-online-outsourcing

[2] Lehdonvirta, V. (2017) The online gig economy grew 26% over the past year, The iLabour Project, Oxford Internet Institute. Available at: http://ilabour.oii.ox.ac.uk/the-online-gig-economy-grew-26-over-the-past-year/

[3] Heeks, R. (2017) Decent Work and the Digital Gig Economy: A Developing Country Perspective on Employment Impacts and Standards in Online Outsourcing, Crowdwork, etc, Centre for Development Informatics, Global Development Institute, University of Manchester. Available at: http://hummedia.manchester.ac.uk/institutes/gdi/publications/workingpapers/di/di_wp71.pdf

[4] Rubec, J. (2017) Study: The Dark Side of the Gig Economy, Cision. Available at: https://www.cision.com/us/2016/12/the-dark-side-of-the-gig-economy/

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Industry 4.0 to Digital Industrialisation: When Digital Technologies meet Industrial Transformation

23 April 2018 1 comment

As digital technologies increasingly permeate all aspects of our physical world, many believe that we are moving into a hyper-connected, intelligent society and economy. One of the emerging concepts underpinning this potential transformation is the Fourth Industrial Revolution, or Industry 4.0.

What is Industry 4.0?

According to the proponents of Industry 4.0, each industrial revolution has shifted manufacturing opportunities and patterns of specialisation, enabled by key technological developments as illustrated in Figure 1.

industry 4.0 timeline2

Figure 1. Industry 4.0 trajectory (source: Author based on [1] and [2])

The vision of Industry 4.0 includes digitalising all elements of industrial activities to achieve a highly flexible, distributed production and service network. Through advancements of technologies such as Artificial Intelligence (AI), advanced automation and robotics, 3D printing, big data and Internet of Things, a tighter integration of digital and physical elements will allow machine-to-machine interactions and a mode of operation that provides more efficient production. In an absolute Industry 4.0 world, every object and all machinery in the factory will be interconnected to share data and operate without much human presence [2].

This of course, is only viable when an advanced level of technological, social and economic integration occurs. Given that technologies progress at an unpredictable rate, and that their real-world applications often lead to unexpected outcomes, it is difficult to know how (or whether) industry 4.0 will manifest. Nevertheless, recent studies warn us that this industrial change can drive uneven global development even further.

Shifting focus from manufacturing to “digital industrialisation”

AI and robotics may take 800 million jobs by 2030 in the world, and emerging economies such as China and India could be hit the hardest, losing 236 and 120 million jobs by 2030 respectively [3]. The costs of operating robots and 3D printers in furniture manufacturing in the US is predicted to be cheaper than Kenyan wages in 2033 [4], indicating that the lower labour cost may no longer be the main attribute ensuring competitiveness in a global market.

Given that industrialisation has long been considered to play a vital role in economic growth of developing countries, the development implications of this transformation have been mainly discussed in manufacturing, albeit with a negative perspective: changing patterns and geography of production [2] (such as re-shoring manufacturing back to high-income countries) and technological unemployment in labour-intensive manufacturing industry [4].

However, I would like to bring more attention to the development of the “digital” side of this industrial transformation – which I will refer to as digital industrialisation. This is a work-in-progress concept that encompasses not only the technological integration of digital technologies into manufacturing, but also the extensive re-organisation of an economy to digitalise production processes.

Some work on this has already been carried out within the DIODE (Development Implications of Digital Economies) network [5], but we need more research to build a better picture of the current and future landscape: for example, how digital industrialisation can take place in small-scale, localised production networks in the global South [6] and how the economic models emerging within the digital economy (such as platform economy and gig economy) may impact innovation and manufacturing processes globally [7].

I will further argue that this impending industrial transformation is best understood as a continuous process rather than a goal to reach – something that terms such as industry 4.0 tend to project. Rather than focusing on the potential winners and losers in this race, we need to elucidate how this transformation can take place in an inclusive and sustainable manner.


[1] Lasi, H., Fettke, P., Kemper, H. G., Feld, T. and Hoffmann, M. (2014) ‘Industry 4.0.’ Business and Information Systems Engineering, 6(4), pp. 239–242.

[2] Hallward-Driemeier, M. and Nayyar, G. (2018) Trouble in the making? The future of Manufacturing-led development. Washington, DC: The World Bank.

[3] Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., Sanghvi, S., (2017) Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

[4] Banga, K. and te Velde, D. (2018) Digitalisation and the future of manufacturing in Africa. London: Overseas Development Institute.

[5] Bukht, R. and Heeks, R. (2017) Defining, conceptualising and measuring the digital economy. GDI Development Informatics Working Paper 68. Centre for Development Informatics, University of Manchester, UK.

[6] Seo-Zindy, R., & Heeks, R. (2017) ‘Researching the emergence of 3D printing, makerspaces, hackerspaces and fablabs in the global south: A scoping review and research agenda on digital innovation and fabrication networks‘, Electronic Journal of Information Systems in Developing Countries, 80(1), pp. 1–24.

[7] UNCTAD (2017) The ‘new’ digital economy and development, UNCTAD Technical Notes on ICT for Development no.8. Geneva: United Nations Conference on Trade And Development.

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