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Using Social Media to Value Public Science

Scientific discoveries advance our understanding of nature. That knowledge is harnessed to solve urgent societal problems and ultimately for the betterment of life. Public and private decision-makers fund the scientists and facilitate them to acquire the resources to pursue their scientific endeavours.  Since scientific knowledge does not become depleted when shared, and once published in the public domain it is available to be accessed by anyone, it can be characterized as public goods. CERN, as a large-scale multinational scientific establishment, presents an ideal example to study the public value of scientific output. The study summarized below, published by researchers at the University Santiago de Compostela as part of the Science Policy Reports book series, constructs the perception of the public towards scientific activities at CERN by analysing big data collected via Twitter posts.

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How is the public value of a scientific undertaking appraised? Two types of values can be attributed to scientific contributions when analysing them as public goods. These attributes, emanating from economic theory, are defined as use-values and non-use values. For science and technology, patents, licenses, and other market realizations determine the use-value. Non-use value is based on perceived potential future worth.

Non-use values have three distinct categorizations. Option value refers to the value that is placed on private willingness to pay for maintaining or preserving a public asset or service even if there is little or no likelihood of ever being used. Bequest value is the value of satisfaction from preserving a natural or historic environment as natural heritage or cultural heritage for future generations. Existence value is the benefit people receive from knowing that a particular environmental resource or asset, such as the Grand Canyon or an endangered species, exists. It is the value of the benefits derived from the asset’s existence alone.

The Total Economic Value (TEV) is an estimation of the sum of the three types of non-use values described above. The estimation of TEV involves many variables and therefore, a long-term approximation could be compounded by the associated uncertainties. In attempting to value science as public goods one could adopt a direct approach, surveying citizens’ response about their preferences, where the outcome is susceptible to varying biases, or an indirect method where information is obtained from the market to acquire insights about how much the public value science. As an example of the latter, the study summarized provides an analysis of the perceptions that citizens have about CERN through information collected from the social media platform Twitter.

Twitter provides unsolicited opinions of its users in the form of “tweets.” Tweets are messages posted on an individual’s Twitter account limited to 140 characters. On Twitter, adding a “#” to the beginning of an unbroken word or phrase creates a hashtag. When a hashtag is used in a tweet, it becomes linked to all of the other tweets that include the same hashtag. The hashtag can be thought of as a specific topic and all the messages and posts by Twitter users on that particular topic can be seen by following the hashtag. At the time when the research review was conducted, Twitter had around 261 million monthly active users worldwide, providing a rich source of data to examine.

The analysis was conducted using data that was collected from Twitter during a 9-month period spanning from October 2018 to  June 2019. Tweets with particular hashtags and keywords were used in obtaining the database to be studied. Specifically, tweets containing the following hashtags were gathered: #CERN, #AtlasExperiment, #LHC News, #CERN-LHCLive, #ALICE Experiment, #CMS Experiment.

The lexicon developed by the National Research Centre Canada (NRC) was applied to analyse the social media dataset from Twitter. Named EmoLex, The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). Around 14,000 words and 25,000 word senses constructed using the Amazon Mechanical Turk (MTurk) are contained within EmoLex. MTurk is a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce who can perform these tasks virtually. The tweets analysed using the EmoLex showed a high correlation in senses reflecting the emotions anticipation and trust. This result suggests that the public perceive the scientific activities conducted at CERN in a generally positive light.

Another method employed to study the data retrieved from Twitter was applying the Hedonometer tool. Launched in 2013, based on research at the University of Vermont Complex Systems Centre, Hedonometer uses English-language tweets to create an average happiness index. To quantify the happiness of words, 5,000 most frequent words from 4 sources are merged. The Sources used are Google Books, New York Times, music lyrics, and Twitter messages. The resulting library contains approximately 10,000 unique words. Hedonometer analyses text by partitioning them into phrases and the phrases into words. Words are associated with scores of positive and negative feelings, and thus a total score is obtained. Using MTurk, each of these words are scored on a scale of (1) sad to (9) happy. The results obtained demonstrate a score of 5.35. Again, a positive reflection by the public when talking about science conducted at CERN.

The results achieved above indicate an overall positive perception towards CERN research activities. Perhaps also a tool to gauge the public’s willingness to pay for the scientific services provided by CERN. This study shows the techniques to employ when analysing big data available through social media applying data science. While the keywords and hashtags used in this study may be biased towards the general public interested in science it proposes a method to investigate the public value of science in general.

 

Sources

Amazon Mechanical Turk (MTurk), https://www.mturk.com

EmoLex, http://www.saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm

Hedonometer, https://hedonometer.org/about.html

Loureiro M.L., Alló M. (2021) How to Value Public Science Employing Social Big Data? In: Beck H.P., Charitos P. (eds) The Economics of Big Science. Science Policy Reports. Springer, Cham. https://doi.org/10.1007/978-3-030-52391-6_13

Science Policy Reports, https://link.springer.com/bookseries/8882

Wilsdon, James & Wynne, Brian & Stilgoe, Jack. (2005). The Public Value of Science (or how to ensure that science really matters). 10.13140/RG.2.1.2281.7449.

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