The pervasiveness of sports betting marketing and advertising is arguably normalising betting behaviour among increasingly larger groups of population. In their adverts, bookmakers represent characters and situations that conventionalise betting and promote specific behaviours while ignoring others. The present study examined a sample of British and Spanish sports betting television adverts (N = 135) from 2014 to 2016 to understand how bettors and betting are being represented. Using content analysis, 31 different variables grouped into seven broad categories were assessed, including general information about the advert, the characters and situations represented, the identification of the characters with sports, the use of online betting, the co-representation of gambling along other risky behaviours such as eating junk food and drinking alcohol, the amount of money wagered, and other variables such as the representation of free bets, humour, and celebrities. The results showed a male-dominant betting representation with no interaction between women. Typically, bettors were depicted surrounded by people but isolated in their betting, emphasising the individual consumption practice that mobile betting promotes. In-play betting was observed in almost half of the adverts. A little empirical evidence indicates that betting while watching sport in betting adverts is associated with emotionally charged situations such as celebrations and/or alcohol drinking. Bettors were typically depicted staking small amounts of money with large potential returns, implying high risk bets. Overall, the study provides preliminary evidence in understanding the social representation of betting behaviour by bookmakers and critiques the problematic consequences of such representation from a public health perspective.

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

Twitter has become a privileged data source for analyzing the behavior of users when interacting online. This research aims to explore the interactive behavior of users in political discussions and the changes in their behavior over time. Understanding the interactive functions of Twitter (retweeting, mentioning, and replying) as digital traces of users’ behavior, we analyze the patterns of interaction of politicians, media, and citizens in two political discussions in Spain during the 2015 and 2016 general elections. Our results confirm previous studies that prove the homophilic behavior of politicians and citizens in political discussions. The networks of interaction, in particular, the retweet network, resemble echo chambers. It also shows that media play the role of weak ties of the networks. The analysis also shows that the patterns of interaction remained stable after the repetition of the election, and only a meager part of the users participating in both discussions changed their behavior. This article aims to contribute to the use of Twitter as a source for understanding people’s interactions is political discussions in social media and their dynamics across time.

The legalisation of online gambling in multiple territories has caused a growth in the exposure of consumers to online sports betting (OSB) advertising. While some efforts have been made to understand the visible structure of betting promotional messages, little is known about the latent components of OSB advertisements. The present study sought to address this issue by examining the metaphorical conceptualisation of OSB advertising. A sample of Spanish and British television OSB advertisements from 2014 to 2016 was analysed (N = 133). Following Lakoff and Johnson’s conceptual metaphor theory, four main structural metaphors that shaped how OSB advertising can be understood were identified: betting as (1) an act of love, (2) a market, (3) a sport, and (4) a natural environment. In general, these metaphors, which were found widely across 29 different betting brands, facilitated the perception of bettors as active players, with an executive role in the sport events bet upon, and greater control over bet outcomes.

In November 9, 2014, the Catalan government called Catalan people to participate in a straw poll about the independence of Catalonia from Spain. This article analyzes the use of Twitter between November 8 and 10, 2014. Drawing on a methodology developed by Guerrero-Solé, Corominas-Murtra, and Lopez-Gonzalez, this work examines the structure of the retweet overlap network (RON), formed by those users whose communities of retweeters have nonzero overlapping, to detect the community structure of the network. The results show a high polarization of the resulting network and prove that the RON is a reliable method to determinate network community structures and users’ political leaning in political discussions.

Social media have changed the communication practices by creating an acute need for continuous interaction. The use of social chatbots is growing as an effective way to communicate with publics. Bots have become social actors and then, someone must account for their actions. Since responsibility is bounded to agency and rationality, it cannot be directly attributed to bots. Who should be held responsible for non-human beings’ actions, particularly when the consequences of these actions are negative? We address this controversy from both theoretical and empirical perspectives. Firstly, we discuss the adequacy of the notions of moral responsibility and accountability regarding non-human artificial agents, as they are ruled by complex, intentionally opaque and unpredictable interactions and processes. We do it from the two approaches currently predominant: context-dependent and structuralist. Secondly, we draw on the assumption that the failure of a computer system is an opportunity to gain knowledge about the interested powers behind its design and functioning. Then, taking the concept of media frame as an implicit way of spotting the agent of the story, we perform an exploratory analysis on how responsibility was attributed by the media in the paradigmatic case of the transformation of Tay, a chatbot launched by Microsoft in 2016, turned into a racist, Nazi and homophobic hate speaker. Our results illustrate the difficulties media experienced in consistently attributing the responsibility for the chatbots’ malfunction. Results show the discourse is, in general, simplistic, non-critical and misleading, and tends to depict a reality that favors business’s interests. We conclude that, while all the actors interacting with the chatbot share the responsibility of its actions, it is only Microsoft who must account for these actions, both retrospectively and prospectively.