How food companies use social media to influence policy debates: a framework of Australian ultra-processed food industry Twitter data

2020 ◽  
pp. 1-12
Author(s):  
Daniel Hunt
Keyword(s):  
Social Media ◽  
Health Policy ◽  
Food Industry ◽  
Self Regulation ◽  
Public Perceptions ◽  
Processed Food ◽  
Policy Debates ◽  
Twitter Data ◽  
Brand Marketing ◽  
Value Judgements

Abstract Objective: To understand if, and how, Australian ultra-processed food industry actors use Twitter to influence food and health policy debates and produce a conceptual framework to describe such influence. Design: Twitter data of prominent industry actors were defined through purposive sampling and inductively coded to investigate possible influence on food and health policy debates. These are described using descriptive statistics and coded extracts. Setting: Australia. Participants: Twitter accounts of nine prominent ultra-processed food industry actors, including major trade associations. Results: Ultra-processed food industry actors actively used Twitter to influence food and health policy debates. Seven overarching strategies were identified: co-opting public health narratives; opposing regulation; supporting voluntary, co- or self-regulation; engaging policy processes and decision-makers; linking regulatory environments to the need for ongoing profitability; affecting public perceptions and value judgements; and using ignorance claims to distort policy narratives. Each lobbying strategy is underpinned with tactics described throughout and captured in a framework. Conclusions: The current study creates a framework to monitor how food industry actors can use social media to influence food and health policy debates. As such, social media appears to be not only an important commercial determinant of health for brand marketing, but also an extension of lobbying practices to reshape public perceptions of corporate conduct and policy-making.

scholarly journals Commercial use of evidence in public health policy: a critical assessment of food industry submissions to global-level consultations on non-communicable disease prevention

2021 ◽  
Vol 6 (8) ◽  
pp. e006176
Author(s):  
Kathrin Lauber ◽  
Darragh McGee ◽  
Anna B Gilmore
Keyword(s):  
Health Policy ◽  
Food Industry ◽  
Communicable Disease ◽  
Self Regulation ◽  
Negative Consequences ◽  
Independent Evidence ◽  
Regulatory Policies ◽  
Non Communicable Disease ◽  
Made In

BackgroundUltra-processed food industry (UPFI) actors have consistently opposed statutory regulation in health policy debates, including at the WHO. They do so most commonly with claims that regulatory policies do not work, will have negative consequences or that alternatives such as self-regulation work well or better. Underlying this are often assertions that industry is aligned with principles of evidence-based policymaking. In this study, we interrogate if this holds true by exploring the extent and quality of the evidence UPFI respondents employed to support claims around regulatory policy, and how they did this.MethodsFirst, we identified all submissions from organisations who overtly represent UPFI companies to consultations held by the WHO on non-communicable disease policy between 2016 and 2018. Second, we extracted all relevant factual claims made in these submissions and noted if any evidence was referenced in support. Third, we assessed the quality of evidence using independence from UPFI, nature, and publication route as indicators. Lastly, where peer-reviewed research was cited, we examined if the claims made could be justified by the source cited.ResultsAcross 26 included consultation responses, factual claims around regulation were made in 18, although only 10 referenced any evidence at all. Of all 114 claims made, 39 pieces of identifiable evidence were cited in support of 56 claims. Of the 39 distinct pieces of evidence, two-thirds were industry-funded or industry-linked, with only 16 externally peer-reviewed. Over half of industry-funded or industry-linked academic articles failed to declare a conflict of interest (COI). Overall, of only six claims which drew on peer-reviewed and independent research, none appropriately represented the source.DiscussionUPFI respondents made far-reaching claims which were rarely supported by high-quality, independent evidence. This indicates that there may be few, if any, benefits from consulting actors with such a clear COI.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Junk Food Ads Reach Children Despite Food Industry Self-regulation

JAMA ◽  
2017 ◽  
Vol 317 (23) ◽  
pp. 2359 ◽  
Author(s):  
Jennifer Abbasi
Keyword(s):  
Food Industry ◽  
Self Regulation ◽  
Junk Food

EXPRESS: The Gift of Data: Industry-Led Food Reformulation and the Obesity Crisis in Europe

2020 ◽  
pp. 074391562098384
Author(s):  
Norah Campbell ◽  
Sarah Browne ◽  
Marius Claudy ◽  
Melissa Mialon ◽  
Hercberg Serge ◽  
...  
Keyword(s):  
Public Health ◽  
Food Industry ◽  
Processed Food ◽  
Policy Makers ◽  
Intelligence System ◽  
Gift Economy ◽  
Nutrition Policies ◽  
The Gift ◽  
Marketing Intelligence ◽  
The Impact

Ultra-processed food manufacturers have proposed that product reformulation should be a key strategy to tackle obesity. In determining the impact of reformulation on population dietary behaviours, policy makers are often dependant on data provided by these manufacturers. Where such data are “gifted” to regulators there may be an implicit expectation of reciprocity that adversely influences nutrition policies. We sought to assess Europe’s industry-led reformulation strategy in five countries deploying critical policy studies as an approach. We found that interim results on industry-led food reformulation did not meet their targets. Information asymmetries exist between food industry and policy makers: the latter are not privy to marketing intelligence and must instead rely on data that are voluntarily donated by food industry actors. These data represent a distorted snippet of the marketing intelligence system from whence they came. Because these data indeed bear all the hallmarks of a gift, regulatory and public health authorities operate within a gift economy. The implications of this “data gift economy” are strategic delay and goal-setting when the field is not visible. Ultimately, this could diminish the implementation of public health nutrition policies that are contrary to the commercial interests of ultra-processed food producers.

Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency

2021 ◽  
pp. 1-41
Author(s):  
Donato VESE
Keyword(s):  
Social Media ◽  
Freedom Of Expression ◽  
Self Regulation ◽  
Public Law ◽  
Fake News ◽  
Human Right ◽  
Social Media Platforms ◽  
Definition Of ◽  
The Right ◽  
Media Censorship

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).

Dynamics of convergence behaviour in social media crisis communication – a complexity perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Milad Mirbabaie ◽  
Stefan Stieglitz ◽  
Felix Brünker
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Crisis Communication ◽  
Design Methodology ◽  
Content Type ◽  
The Public ◽  
Analysis Techniques ◽  
Twitter Data ◽  
Convergence Behaviour ◽  
Over Time

PurposeThe purpose of this study is to investigate communication on Twitter during two unpredicted crises (the Manchester bombings and the Munich shooting) and one natural disaster (Hurricane Harvey). The study contributes to understanding the dynamics of convergence behaviour archetypes during crises.Design/methodology/approachThe authors collected Twitter data and analysed approximately 7.5 million relevant cases. The communication was examined using social network analysis techniques and manual content analysis to identify convergence behaviour archetypes (CBAs). The dynamics and development of CBAs over time in crisis communication were also investigated.FindingsThe results revealed the dynamics of influential CBAs emerging in specific stages of a crisis situation. The authors derived a conceptual visualisation of convergence behaviour in social media crisis communication and introduced the terms hidden and visible network-layer to further understanding of the complexity of crisis communication.Research limitations/implicationsThe results emphasise the importance of well-prepared emergency management agencies and support the following recommendations: (1) continuous and (2) transparent communication during the crisis event as well as (3) informing the public about central information distributors from the start of the crisis are vital.Originality/valueThe study uncovered the dynamics of crisis-affected behaviour on social media during three cases. It provides a novel perspective that broadens our understanding of complex crisis communication on social media and contributes to existing knowledge of the complexity of crisis communication as well as convergence behaviour.

Sign in / Sign up

Export Citation Format

Share Document