Full length articleBeyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events
Introduction
Social media applications host a large volume of opinions that reflect people's reaction to events. Even as brief as Twitter's 140 characters, social media reactions function as user-driven data that can be for example automatically classified in terms of their sentiment using opinion mining or machine learning techniques (e.g. Ghiassi, Skinner, & Zimbra, 2013; Thelwall, Buckley, Paltoglou, Cai, & Kappas, 2010). Analysis of social media data seems particularly useful when unexpected and potentially stressful events occur and there is need of understanding how Internet users are making sense of them. Sentiment analysis over a large volume of user-generated data have been used for example in rapid reputation assessments (e.g. brand management, political marketing) or as an indication of how digital publics respond to events, associated with television shows, football games, significant news or other meaningful events (see e.g. Brooker et al., 2013, Highfield et al., 2013, Thelwall et al., 2011).
Reactions collected from social media have also become central in situations of high uncertainty and high demands on individuals and communities (see e.g. Heverin and Zach, 2012, Palen et al., 2010). These situations involve a deviation from the “normal” state that existed before their occurrence and may evidence the presence of a stressful experience, given that people have to draw on resources to cope with demands, which they would not “normally” have to (Gaspar, Barnett, & Seibt, 2015). This is the case with unexpected and potentially stressful events associated with the emergence of health threats (e.g. epidemics, biological and chemical contamination of food), terrorist attacks, natural disasters (e.g. hurricanes, floods), industrial accidents (e.g. nuclear) or even sudden events related with macroeconomic changes, that may come to be perceived as crisis. Reactions during these events can evidence collective sense-making (Gilles et al., 2013), supportive actions (Murthy, 2013, Panagiotopoulos et al., 2014), social sharing of emotions and empatic concerns for affected individuals (Neubaum, Rösner, Rosenthal-von der Pütten, & Krämer, 2014) and individual strategies of approach/avoidance (Jonas et al., 2014), that would be less prevalent in non stressful situations with lower demands to cope with. In this context, opinion mining and sentiment analysis techniques can be deployed to support response coordination (Purohit et al., 2013) or provide information about which audiences might be affected by emerging risk events (Lachlan, Spence, & Lin, 2014).
There are however valid reasons to believe that computer-based sentiment analysis techniques may not be adequate to assess social media reactions on their own; and that another complementary layer of human-based assessment should be put forward. More than triggering positive or negative affective reactions, potentially stressful events can be perceived both at the individual and social levels as posing threats or challenges to cope with, depending on the resources available and the demands posed by them (e.g. Blascovich & Mendes, 2001). Moreover, these perceptions may change over time, along the chain of events that might occur - the hazard sequence (Barnett & Breakwell, 2003). This type of assessment requires a deeper understanding of people's affective expressions on social media and the context in which people express sentiment and other cognitive and behavioural manifestations while events unfold.
Specifically, this paper makes the case that alongside assessments of affective or sentiment valence (positive, negative, neutral, ambivalent), a qualitative analysis of expressions while unexpected events unfold can serve a deeper understanding of the specific functions that users' emotional states may reflect. Looking into these functions can allow for an assessment of: 1) how the overall situation is perceived – as a threat or challenge – and 2) the individual and social resources that individuals experience to have, to cope with demands. To identify these two aspects, we adopted the classification scheme by Skinner, Edge, Altman, and Sherwood (2003), which lists 12 higher-order categories or families of coping and three higher-order adaptive function categories. The framework was applied on a Twitter dataset collected in Germany during the 2011 EHEC outbreak in Europe (Escherichia coli contamination of food incident). Food-related crises like contamination incidents tend to generate substantial reactions from the concerned public, which can escalate in unusual ways on social media (Mou and Lin, 2014, Rutsaert et al., 2013). The first cases of EHEC contamination in the food chain were identified in Germany in May 2011, but it was not until July 2011 that the original source was identified and eliminated. Uncertainty over the source and extent of the contamination amplified public reaction and had severe economic and political impact, due to an incorrect attribution of blame to Spanish cucumbers as the original contaminated product (Gaspar et al., 2014).
After elaborating on the importance of qualitative sentiment analysis as the basis for a human-based assessment of reactions to stressful events, we present the study methodology and findings. Based on the latter, we discuss the value of qualitative sentiment analysis particularly in terms of how it can guide efforts to provide the means and resources so that the public can reinterpret an unexpected stressful event(s) as a challenge rather than as a threat.
Section snippets
Why analyse affective expressions on social media beyond positive or negative?
Quantitative sentiment analysis methods can be relevant for all types of analyses that focus on what Sheth, Purohit, Jadhav, Kapanipathi and Chen (2010, p.1) refer to as “event-centric user generated content on social networks”. Popular sentiment analysis techniques like SentiStrength classify messages as having a positive or negative “tone” based on whether they contain at least one positive or negative keyword (e.g. Kramer, Guillory & Hancock, 2014; Thelwall et al. 2011). Strength of the
Sentiment expression as a goal-based way of coping: a guiding framework
The coping concept commonly found in the health psychology literature, may be defined as the “process of attempting to manage the demands created by stressful events that are appraised as taxing or exceeding a person's resources” (Taylor & Stanton, 2007, p.378). These stressful events demand the implementation of affective, cognitive and behavioural coping strategies that would not be implemented under “normal” conditions (Gaspar et al., 2015). Such strategies are determined by two types of
Context and aims
Food crises can provide an illustrating case of emerging affective expressions from unexpected events, since food-related incidents are not seen as inevitable, for example, compared to natural disasters. There are incontestable expectations such as for example that “food should be safe to eat and free of harmful E. coli contamination” (Sellnow & Seeger, 2013, p. 5) and that eating food should not have health consequences as severe as death. The 2011 EHEC outbreak from contaminated food
Results
In order to provide evidence with regard to the first study aim and demonstrate the variety/diversity of affective expressions on Twitter as the crisis unfolded, this section will first present examples of coping expressions in the affective dimension, associated with the 12 coping families. This will be followed by the presentation of examples of functions that coping may have served as the crisis unfolded and by examples of affective expressions within each of the three categories, thus
Discussion
Human-based qualitative sentiment analysis can be complex, time-consuming as well as not as scalable or easily comprehensible as a quantitative report of sentiment. Nevertheless, it can provide additional depth and diversity about how people are coping with an unexpected and potentially stressful social events and the context in which they express themselves. Accordingly, affective expressions in our Twitter dataset varied not only in terms of positive or negative valence but also in terms of:
Conclusions
This study sought to examine the importance of qualitative sentiment analysis of social media reactions to unexpected and potentially stressful social events based on the case of the EHEC crisis in Germany in 2011. Drawing on the framework by Skinner et al. (2003) which allowed analysing a dataset of Twitter expressions, we aimed to show that positive and negative sentiment is not necessarily good or bad but rather potentially informative of people's state and goals to adapt to an emerging
Acknowledgements
This manuscript was developed as part of FoodRisC project - Food Risk Communication - Perceptions and communication of food risks/benefits across Europe: development of effective communication strategies, funded by the European Commission under the 7th Framework Programme - Grant Agreement n. 245124. The authors would like to acknowledge all project partners for the work done during the project, which allowed for the development of this paper and specifically to Josephine Wills (EUFIC) and
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