Home > Digital Development > Social Media Analytics for Better Understanding of the Digital Gig Economy

Social Media Analytics for Better Understanding of the Digital Gig Economy

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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  1. anandsheombar
    27 April 2018 at 11:39 am

    Very interesting. Will the project cover multiple languages or focus on English? Regarding this sentiment analysis, will they analyse the use emojis or memes too?
    Prof. Richard Heeks has also shared this blog in the Facebook group “Social media for Development” https://www.facebook.com/groups/SoMe4D/

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