ResearchPad - facebook https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Left powerless: A qualitative social media content analysis of the Dutch #breakthesilence campaign on negative and traumatic experiences of labour and birth]]> https://www.researchpad.co/article/elastic_article_13813 Disrespect and abuse during labour and birth are increasingly reported all over the world. In 2016, a Dutch client organization initiated an online campaign, #genoeggezwegen (#breakthesilence) which encouraged women to share negative and traumatic maternity care experiences. This study aimed (1) to determine what types of disrespect and abuse were described in #genoeggezwegen and (2) to gain a more detailed understanding of these experiences.MethodsA qualitative social media content analysis was carried out in two phases. (1) A deductive coding procedure was carried out to identify types of disrespect and abuse, using Bohren et al.’s existing typology of mistreatment during childbirth. (2) A separate, inductive coding procedure was performed to gain further understanding of the data.Results438 #genoeggezwegen stories were included. Based on the typology of mistreatment during childbirth, it was found that situations of ineffective communication, loss of autonomy and lack of informed consent and confidentiality were most often described. The inductive analysis revealed five major themes: ‘‘lack of informed consent”; ‘‘not being taken seriously and not being listened to”; ‘‘lack of compassion”; ‘‘use of force”; and ‘‘short and long term consequences”. “Left powerless” was identified as an overarching theme that occurred throughout all five main themes.ConclusionThis study gives insight into the negative and traumatic maternity care experiences of Dutch women participating in the #genoeggezwegen campaign. This may indicate that disrespect and abuse during labour and birth do happen in the Netherlands, although the current study gives no insight into prevalence. The findings of this study may increase awareness amongst maternity care providers and the community of the existence of disrespect and abuse in Dutch maternity care, and encourage joint effort on improving care both individually and systemically/institutionally. ]]> <![CDATA["Clicks, likes, shares and comments" a systematic review of breast cancer screening discourse in social media]]> https://www.researchpad.co/article/N8d8d3073-6769-4a60-aed8-e2beb958c228

Background

Unsatisfactory participation rate at population based organised breast cancer screening is a long standing problem. Social media, with 3.2 billion users in 2019, is potentially an important site of breast cancer related discourse. Determining whether these platforms might be used as channels by screening providers to reach under-screened women may have considerable public health significance.

Objectives

By systematically reviewing original research studies on breast cancer related social media discourse, we had two aims: first, to assess the volume, participants and content of breast screening social media communication and second, to find out whether social media can be used by screening organisers as a channel of patient education.

Methods

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). After searching PubMed, ScienceDirect, Web of Science, Springer and Ebsco, 17 studies were found that met our criteria. A systematic narrative framework was used for data synthesis. Owing to the high degree of heterogeneity in social media channels, outcomes and measurement included in this study, a meta-analytic approach was not appropriate.

Results

The volume of breast cancer related social media discourse is considerable. The majority of participants are lay individuals as opposed to healthcare professionals or advocacy groups. The lay misunderstandings surrounding the harms and benefits of mammography is well mirrored in the content of social media discourse. Although there is criticism, breast cancer screening sentiment on the social media ranges from the neutral to the positive. Social media is suitable for offering peer emotional support for potential participants.

Conclusion

Dedicated breast screening websites operated by screening organisers would ensure much needed quality controlled information and also provide space for reliable question and answer forums, the sharing of personal experience and the provision of peer and professional support.

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<![CDATA[Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas]]> https://www.researchpad.co/article/5c61e910d5eed0c48496f765

Much research has examined how crime rates vary across urban neighborhoods, focusing particularly on community-level demographic and social characteristics. A parallel line of work has treated crime at the individual level as an expression of certain behavioral patterns (e.g., impulsivity). Little work has considered, however, whether the prevalence of such behavioral patterns in a neighborhood might be predictive of local crime, in large part because such measures are hard to come by and often subjective. The Facebook Advertising API offers a special opportunity to examine this question as it provides an extensive list of “interests” that can be tabulated at various geographic scales. Here we conduct an analysis of the association between the prevalence of interests among the Facebook population of a ZIP code and the local rate of assaults, burglaries, and robberies across 9 highly populated cities in the US. We fit various regression models to predict crime rates as a function of the Facebook and census demographic variables. In general, models using the variables for the interests of the whole adult population on Facebook perform better than those using data on specific demographic groups (such as Males 18-34). In terms of predictive performance, models combining Facebook data with demographic data generally have lower error rates than models using only demographic data. We find that interests associated with media consumption and mating competition are predictive of crime rates above and beyond demographic factors. We discuss how this might integrate with existing criminological theory.

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<![CDATA[PopRank: Ranking pages’ impact and users’ engagement on Facebook]]> https://www.researchpad.co/article/5c58d664d5eed0c484031d68

The advent of social networks revolutionized the way people access to information sources. Understanding the complex relationship between these sources and users is crucial. We introduce an algorithm, that we call PopRank, to assess both the Impact of Facebook pages as well as users’ Engagement on the basis of their mutual interactions. The ideas behind the PopRank are that i) high impact pages attract many users with a low engagement, which means that they receive comments from users that rarely comment, and ii) high engagement users interact with high impact pages, that is they mostly comment pages with a high popularity. The resulting ranking of pages can predict the number of comments a page will receive and the number of its future posts. Pages’ impact turns out to be slightly dependent on the quality of pages’ informative content (e.g., science vs conspiracy) but independent of users’ polarization.

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<![CDATA[Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case study]]> https://www.researchpad.co/article/5c6448e7d5eed0c484c2f17d

A recurrent criticism concerning the use of online social media data in political science research is the lack of demographic information about social media users. By employing a face-recognition algorithm to the profile pictures of Facebook users, the paper derives two fundamental demographic characteristics (age and gender) of a sample of Facebook users who interacted with the most relevant British parties in the two weeks before the Brexit referendum of 23 June 2016. The article achieves the goals of (i) testing the precision of the algorithm, (ii) testing its validity, (iii) inferring new evidence on digital mobilisation, and (iv) tracing the path for future developments and application of the algorithm. The findings show that the algorithm is reliable and that it can be fruitfully used in political and social sciences both to confirm the validity of survey data and to obtain information from populations that are generally unavailable within traditional surveys.

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<![CDATA[A viral video and pet lemurs on Twitter]]> https://www.researchpad.co/article/5c3fa5b1d5eed0c484ca76f7

Content shared on social media platforms can impact public perceptions of wildlife. These perceptions, which are in part shaped by context (e.g. non-naturalistic setting, presence of a human), can influence people’s desires to interact with or acquire wild animals as pets. However, few studies have examined whether this holds true for wild animals featured in viral videos. This study reports on opportunistic data collected on Twitter before, during, and after a video that featured a habituated ring-tailed lemur (Lemur catta), called “Sefo”, in southern Madagascar went ‘viral’ (i.e. circulated rapidly on the internet). Our dataset of 13,953 tweets (from an 18.5-week time period in early 2016) referencing lemurs was collected using targeted keywords on the Twitonomy Service. We identified 613 individual tweets about people wanting a lemur as a pet. In addition, 744 tweets that were captured in our dataset linked to the Sefo viral video. We found that as the number of tweets about the viral video increased, so did the number of tweets where an individual wanted to have a lemur as a pet. Most tweets (91%) did not make reference to a specific species of lemur, but when they did, they often (82%) referenced ring-tailed lemurs (L. catta), ruffed lemurs (Varecia spp.), and mouse lemurs (Microcebus spp.). This study serves as a case study to consider how viral content can impact how wild animals are perceived. We close by noting that social media sites like Twitter, which are increasingly providing their users with news and information, should carefully consider how information about wild animals is shared on their platforms, as it may impact animal welfare.

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<![CDATA[Social media popularity and election results: A study of the 2016 Taiwanese general election]]> https://www.researchpad.co/article/5c0841f0d5eed0c484fcb37b

This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election.

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<![CDATA[What demographic attributes do our digital footprints reveal? A systematic review]]> https://www.researchpad.co/article/5c0841bad5eed0c484fca901

To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.

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<![CDATA[Latent human traits in the language of social media: An open-vocabulary approach]]> https://www.researchpad.co/article/5c08417fd5eed0c484fc9c19

Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data—language use—at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use.

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<![CDATA[“Anyone Know What Species This Is?” – Twitter Conversations as Embryonic Citizen Science Communities]]> https://www.researchpad.co/article/5989da1dab0ee8fa60b7db27

Social media like blogs, micro-blogs or social networks are increasingly being investigated and employed to detect and predict trends for not only social and physical phenomena, but also to capture environmental information. Here we argue that opportunistic biodiversity observations published through Twitter represent one promising and until now unexplored example of such data mining. As we elaborate, it can contribute to real-time information to traditional ecological monitoring programmes including those sourced via citizen science activities. Using Twitter data collected for a generic assessment of social media data in ecological monitoring we investigated a sample of what we denote biodiversity observations with species determination requests (N = 191). These entail images posted as messages on the micro-blog service Twitter. As we show, these frequently trigger conversations leading to taxonomic determinations of those observations. All analysed Tweets were posted with species determination requests, which generated replies for 64% of Tweets, 86% of those contained at least one suggested determination, of which 76% were assessed as correct. All posted observations included or linked to images with the overall image quality categorised as satisfactory or better for 81% of the sample and leading to taxonomic determinations at the species level in 71% of provided determinations. We claim that the original message authors and conversation participants can be viewed as implicit or embryonic citizen science communities which have to offer valuable contributions both as an opportunistic data source in ecological monitoring as well as potential active contributors to citizen science programmes.

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<![CDATA[The Role of Temporal Trends in Growing Networks]]> https://www.researchpad.co/article/5989da59ab0ee8fa60b8f739

The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment.

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<![CDATA[With whom do you feel most intimate?: Exploring the quality of Facebook friendships in relation to similarities and interaction behaviors]]> https://www.researchpad.co/article/5989db59ab0ee8fa60bdf172

It is widely accepted that people tend to associate more and feel closer to those who share similar attributes with themselves. Most of the research on the phenomenon has been carried out in face-to-face contexts. However, it is necessary to study the phenomenon in computer-mediated contexts as well. Exploring Facebook is important in that friendships within the network indicate a broader spectrum of friends, ranging from complete strangers to confiding relations. Also, since diverse communication methods are available on Facebook, which method a user adopts to interact with a “friend” could influence the quality of the relationship, i.e. intimacy. Thus, current research aims to test whether people in computer-mediated contexts do perceive more intimacy toward friends who share similar traits, and further, aims to examine which interaction methods influence the closeness of relationship by collecting activity data of users on Facebook. Results from current study show traits related to intimacy in the online context of Facebook. Moreover, in addition to the interaction type itself, direction of the interaction influenced how intimate users feel towards their friends. Overall findings suggest that further investigation on the dynamics of online communication methods used in developing and maintaining relationships is necessary.

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<![CDATA[Link-Prediction Enhanced Consensus Clustering for Complex Networks]]> https://www.researchpad.co/article/5989db37ab0ee8fa60bd3604

Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that “consume” the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

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<![CDATA[Footprints of Fascination: Digital Traces of Public Engagement with Particle Physics on CERN's Social Media Platforms]]> https://www.researchpad.co/article/5989da41ab0ee8fa60b89e4b

Although the scientific community increasingly recognizes that its communication with the public may shape civic engagement with science, few studies have characterized how this communication occurs online. Social media plays a growing role in this engagement, yet it is not known if or how different platforms support different types of engagement. This study sets out to explore how users engage with science communication items on different platforms of social media, and what are the characteristics of the items that tend to attract large numbers of user interactions. Here, user interactions with almost identical items on five of CERN's social media platforms were quantitatively compared over an eight-week period, including likes, comments, shares, click-throughs, and time spent on CERN's site. The most popular items were qualitatively analyzed for content features. Findings indicate that as audience size of a social media platform grows, the total rate of engagement with content tends to grow as well. However, per user, engagement tends to decline with audience size. Across all platforms, similar topics tend to consistently receive high engagement. In particular, awe-inspiring imagery tends to frequently attract high engagement across platforms, independent of newsworthiness. To our knowledge, this study provides the first cross-platform characterization of public engagement with science on social media. Findings, although focused on particle physics, have a multidisciplinary nature; they may serve to benchmark social media analytics for assessing science communication activities in various domains. Evidence-based suggestions for practitioners are also offered.

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<![CDATA[Women are Warmer but No Less Assertive than Men: Gender and Language on Facebook]]> https://www.researchpad.co/article/5989dafbab0ee8fa60bc493e

Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and—contrary to previous findings—slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale.

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<![CDATA[Design and Implementation of an Interactive Web-Based Near Real-Time Forest Monitoring System]]> https://www.researchpad.co/article/5989da44ab0ee8fa60b8b15d

This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite based disturbance alerts with the end-user communities to enhance the collection of ground data. The system is developed using open source technologies and has been implemented together with local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system is able to provide easy access to information on forest change and considerably improves the collection and storage of ground observation by local experts. Social media leads to higher levels of user interaction and noticeably improves communication among stakeholders. Finally, an evaluation of the system confirms the usability of the system in Ethiopia. The implemented system can provide a foundation for an operational forest monitoring system at the national level for REDD+ MRV applications.

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<![CDATA[Please Like Me: Facebook and Public Health Communication]]> https://www.researchpad.co/article/5989da65ab0ee8fa60b91d09

Facebook, the most widely used social media platform, has been adopted by public health organisations for health promotion and behaviour change campaigns and activities. However, limited information is available on the most effective and efficient use of Facebook for this purpose. This study sought to identify the features of Facebook posts that are associated with higher user engagement on Australian public health organisations’ Facebook pages. We selected 20 eligible pages through a systematic search and coded 360-days of posts for each page. Posts were coded by: post type (e.g., photo, text only etc.), communication technique employed (e.g. testimonial, informative etc.) and use of marketing elements (e.g., branding, use of mascots). A series of negative binomial regressions were used to assess associations between post characteristics and user engagement as measured by the number of likes, shares and comments. Our results showed that video posts attracted the greatest amount of user engagement, although an analysis of a subset of the data suggested this may be a reflection of the Facebook algorithm, which governs what is and is not shown in user newsfeeds and appear to preference videos over other post types. Posts that featured a positive emotional appeal or provided factual information attracted higher levels of user engagement, while conventional marketing elements, such as sponsorships and the use of persons of authority, generally discouraged user engagement, with the exception of posts that included a celebrity or sportsperson. Our results give insight into post content that maximises user engagement and begins to fill the knowledge gap on effective use of Facebook by public health organisations.

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<![CDATA[Windows Instant Messaging App Forensics: Facebook and Skype as Case Studies]]> https://www.researchpad.co/article/5989daf6ab0ee8fa60bc2ee3

Instant messaging (IM) has changed the way people communicate with each other. However, the interactive and instant nature of these applications (apps) made them an attractive choice for malicious cyber activities such as phishing. The forensic examination of IM apps for modern Windows 8.1 (or later) has been largely unexplored, as the platform is relatively new. In this paper, we seek to determine the data remnants from the use of two popular Windows Store application software for instant messaging, namely Facebook and Skype on a Windows 8.1 client machine. This research contributes to an in-depth understanding of the types of terrestrial artefacts that are likely to remain after the use of instant messaging services and application software on a contemporary Windows operating system. Potential artefacts detected during the research include data relating to the installation or uninstallation of the instant messaging application software, log-in and log-off information, contact lists, conversations, and transferred files.

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<![CDATA[Significance of likes: Analysing passive interactions on Facebook during campaigning]]> https://www.researchpad.co/article/5989db5fab0ee8fa60be10f9

With more and more political candidates using social media for campaigning, researchers are looking at measuring the effectiveness of this medium. Most research, however, concentrates on the bare count of likes (or twitter mentions) in an attempt to correlate social media presence and winning. In this paper, we propose a novel method, Interaction Strength Plot (IntS) to measure the passive interactions between a candidate’s posts on Facebook and the users (liking the posts). Using this method on original Malaysian General Election (MGE13) and Australian Federal Elections (AFE13) Facebook Pages (FP) campaign data, we label an FP as performing well if both the posting frequency and the likes gathered are above average. Our method shows that over 60% of the MGE13 candidates and 85% of the AFE13 candidates studied in this paper had under-performing FP. Some of these FP owners would have been identified as popular based on bare count. Thus our performance chart is a vital step forward in measuring the effectiveness of online campaigning.

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<![CDATA[Diageo's 'Stop Out of Control Drinking' Campaign in Ireland: An Analysis]]> https://www.researchpad.co/article/5989da9dab0ee8fa60ba46bc

Background

It has been argued that the alcohol industry uses corporate social responsibility activities to influence policy and undermine public health, and that every opportunity should be taken to scrutinise such activities. This study analyses a controversial Diageo-funded ‘responsible drinking’ campaign (“Stop out of Control Drinking”, or SOOCD) in Ireland. The study aims to identify how the campaign and its advisory board members frame and define (i) alcohol-related harms, and their causes, and (ii) possible solutions.

Methods

Documentary analysis of SOOCD campaign material. This includes newspaper articles (n = 9), media interviews (n = 11), Facebook posts (n = 92), and Tweets (n = 340) produced by the campaign and by board members. All material was coded inductively, and a thematic analysis undertaken, with codes aggregated into sub-themes.

Results

The SOOCD campaign utilises vague or self-defined concepts of ‘out of control’ and ‘moderate’ drinking, tending to present alcohol problems as behavioural rather than health issues. These are also unquantified with respect to actual drinking levels. It emphasises alcohol-related antisocial behaviour among young people, particularly young women. In discussing solutions to alcohol-related problems, it focuses on public opinion rather than on scientific evidence, and on educational approaches and information provision, misrepresenting these as effective. “Moderate drinking” is presented as a behavioural issue (“negative drinking behaviours”), rather than as a health issue.

Conclusions

The ‘Stop Out of Control Drinking’ campaign frames alcohol problems and solutions in ways unfavourable to public health, and closely reflects other Diageo Corporate Social Responsibility (CSR) activity, as well as alcohol and tobacco industry strategies more generally. This framing, and in particular the framing of alcohol harms as a behavioural issue, with the implication that consumption should be guided only by self-defined limits, may not have been recognised by all board members. It suggests a need for awareness-raising efforts among the public, third sector and policymakers about alcohol industry strategies.

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