Over the past 40 years, we have witnessed seismic shifts in advertising planning and buying processes. Due in no small part to the emergence of digital media, consumer choices have mushroomed, while advertisers understand much more about target audiences. Advertising activities have been drastically transformed by the possibilities that technology creates for targeting and measurement, automation of activities via programmatic advertising, and an overall computational approach in which algorithmic, data-driven decisions dominate. In this era, what does it mean to “do media planning” and to do it well? The present article argues for planning decisions to move away from simply purchasing exposure to instead focusing on fostering engagement through meaningful and sustained interactions with consumers. It provides an overview of the digital ecosystem that makes computational advertising possible, updates the notion of consumer engagement for this context, and reviews how measurement becomes more central to media planning decisions. Ethical and normative considerations and computational advertising as an adaptive learning system are discussed as crosscutting issues, followed by a proposed research agenda.

Chatbots are a burgeoning opportunity for news media outlets to disseminate their content in a conversational way, and create an engaging experience around it. Since chatbots are social and interactive technologies, they might be effective tools to lower the threshold of engaging with news content containing opposing views. In an experiment, we test this idea by investigating whether people are more likely to accept a news article containing conflicting views when it is delivered by a chatbot, as compared with the same article on a news website. The results indicated that people agreed more to a counter-attitudinal news article when it was delivered by a news chatbot (compared with the website article). In addition, users also perceived this chatbot article as more credible. The underlying process for this effect was that people attributed human-like characteristics to the chatbot on an implicit level (i.e., perceived mindless anthropomorphism). These results are discussed in the light of their potential contribution to an informed public discourse and a decrease in polarization in our society.

Prior online health research has mainly focused on the predictors or outcomes of online health information, leaving online health information itself understudied. Therefore, online health information has remained an umbrella term encompassing different platforms (expert- vs. peer-generated). A hybrid method that combines qualitative and computational methods is used to identify different topics discussed on these different platforms, and an initial model of patients’ social support needs was developed and applied to data obtained from the hybrid method. Using topic modeling (Nposts = 52.990), topics on two expert- and two peer-generated platforms were identified. Differences between and within platforms were found. While peer-generated platforms mainly covered interaction on emotional support topics, expert-generated platforms covered informational topics. Within peer-generated platforms, patients used their experiences differently.

The aim of the study was to deepen our understanding of how related multiscreening affects audience memory and persuasion. A survey was administered after a live television show. The results showed that the higher the perceived relatedness of the multi-screen activity, the more persuasive the message. This effect was mediated by subsequent attention to television content, program involvement, and attention to the commercial break. The model was replicated for three different multiscreen activities: social media use, chatting, and information search. Furthermore, it was found that related multiscreening increased the likelihood of respondents staying tuned to the television after the show.

Conversational agents in the form of chatbots available in messaging platforms, or smartphone and home-based virtual assistants, are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies using computational methods. CART is an alternative to Wizard of Oz studies, in which researchers usually resort to (human) research assistants to pose as automated conversational agents. This paper provides an overview of the tool, discusses the role of computational methods in enabling research with conversational agents, and provides a step-by- step tutorial of to design an experiment with a chatbot.

Conversational agents are gradually being deployed by organizations in service settings to communicate with and solve problems together with consumers. The current study investigates how consumers’ perceptions of cooperation with conversational agents in a service context are associated with their perceptions about agents’ anthropomorphism, social presence, the quality of the information provided by an agent, and the agent service performance. An online experiment was conducted in which participants performed a serviceoriented task with the assistance of conversational agents developed specifically for the study and evaluated the performance and attributes of the agents. The results suggest a direct positive link between perceiving a conversational agent as cooperative and perceiving it to be more anthropomorphic, with higher levels of social presence and providing better information quality. Moreover, the results also show that the link between perceiving an agent as cooperative and the agent’s service performance is mediated by perceptions of the agent’s anthropomorphic cues and the quality of the information provided by the agent.

With the “visual turn” online, images have become increasingly important for companies to attract stakeholders to their online CSR communication. To investigate in how far businesses’ visual sustainability language reflects a balanced triple bottom line, this study compared the most profitable European corporations’ websites images (N = 21,841) and visual Twitter posts (N = 3,637) through automated content analysis using computer vision algorithms. The findings of this big data-analysis reveal that European companies overemphasize the financial bottom line on both owned and shared media. The channel matters as firms are more likely to communicate people-, planet-, and profit-related images via social media than their website. Corporations from environmentally sensitive industries tend to highlight the social dimension, though this is where they impact less, and do so more often through their website. Thus, this study confirms the criticism that the business case is dominant in CSR strategies also for visual communication.

Chatbots are increasingly used in a commercial context to make product- or service-related recommendations. By doing so, they collect personal information of the user, similar to other online services. While privacy concerns in an online (website-) context are widely studied, research in the context of chatbot-interaction is lacking. This study investigates the extent to which chatbots with human-like cues influence perceptions of anthropomorphism (i.e., attribution of human-like characteristics), privacy concerns, and consequently, information disclosure, attitudes and recommendation adherence. Findings show that a human-like chatbot leads to more information disclosure, and recommendation adherence mediated by higher perceived anthropomorphism and subsequently, lower privacy concerns in comparison to a machine-like chatbot. This result does not hold in comparison to a website; human-like chatbot and website were perceived as equally high in anthropomorphism. The results show the importance of both mediating concepts in regards to attitudinal and behavioral outcomes when interacting with chatbots.

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed.

This article scrutinizes the method of automated content analysis to measure the tone of news coverage. We compare a range of off-the-shelf sentiment analysis tools to manually coded economic news as well as examine the agreement between these dictionary approaches themselves. We assess the performance of five off-the-shelf sentiment analysis tools and two tailor-made dictionary-based approaches. The analyses result in five conclusions. First, there is little overlap between the off-the-shelf tools; causing wide divergence in terms of tone measurement. Second, there is no stronger overlap with manual coding for short texts (i.e., headlines) than for long texts (i.e., full articles). Third, an approach that combines individual dictionaries achieves a comparably good performance. Fourth, precision may increase to acceptable levels at higher levels of granularity. Fifth, performance of dictionary approaches depends more on the number of relevant keywords in the dictionary than on the number of valenced words as such; a small tailor-made lexicon was not inferior to large established dictionaries. Altogether, we conclude that off-the-shelf sentiment analysis tools are mostly unreliable and unsuitable for research purposes – at least in the context of Dutch economic news – and manual validation for the specific language, domain, and genre of the research project at hand is always warranted.

The purpose of this paper is to explore the interrelationship between television (TV) consumption (viewing ratings), engagement behaviors of different actors on Twitter (TV programs, media, celebrities and viewers) and the content of engagement behaviors (affective, program-related and social content). METHODS. TV ratings and Twitter data were obtained. The content of tweets was analyzed by means of a sentiment analysis. A vector auto regression model was used to understand the interrelationship between tweets of different actors and TV consumption. FINDINGS. First, the results showed a negative interrelationship between TV viewing and viewers’ tweeting behavior. Second, tweets by celebrities and media exhibited similar patterns and were both affected mostly by the number of tweets by viewers. Finally, the content of tweets matters. Affective tweets positively relate to TV viewing, and program-related and social content positively relates to the number of tweets by viewers. IMPLICATIONS. The findings help us understand the online engagement ecosystem and provide insights into drivers of TV consumption and online engagement of different actors. The results indicate that content producers may want to focus on stimulating affective conversations on Twitter to trigger more online and offline engagement. The results also call for rethinking the meaning of TV metrics. ORIGINALITY. While some studies have explored viewer interactions on Twitter, only a few studies have looked at the effects of such interactions on variables outside of social media, such as TV consumption. Moreover, the authors study the interrelations between Twitter interactions with TV consumption, which allows us to examine the effect of online engagement on offline behaviors and vice versa. Finally, the authors take different actors into account when studying real-life online engagement.

Social Networking Sites (SNSs) not only enable users to read or create content about brands, but also to easily pass along this content using information diffusion mechanisms such as retweeting or sharing. While these capabilities can be optimal for viral marketing, little is known, however, about how reading brand messages passed along by SNS contacts influences online brand communication outcomes. Results of a survey with active SNS users indicate that (1) message evaluation, (2) the relationship with the sender, and (3) the receiver’s opinion leadership and opinion-seeking levels influence not only the receiver’s intention to pass along the message further, but also his or her attitude towards the brand. The implications of these findings are discussed, including how these capabilities brought on by SNSs change the brand-consumer relationship online.

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.

Social media have enabled sports fans to interact with their favourite clubs, players, and fellow fans. By using a sample of over 4.5 million tweets, we applied a social networks approach to examine whether and, if so, how different types of users influence online engagement and patterns of information flow of professional football clubs on Twitter. We focus on five types of social mediators (i.e., key users who connect organizations with their publics): (1) organizational (e.g., teams or players), (2) industry (e.g., competitors or associations), (3) media (e.g., journalists), (4) individual (e.g., fans), and (5) celebrities. Our results indicate that the power of media social mediators—the most traditional mediators—has declined over recent years, and they were negatively associated with engagement on Twitter. Instead, relationships between football clubs and publics were primarily mediated by individual social mediators, for top division clubs in particular. Taken together, scholars and practitioners should recognize the potential impact of social mediators, given that even individuals can function as powerful users in the information diffusion process.

Consumers across the globe increasingly engage with user-generated content about brands on social networking sites (i.e., brand-related user-generated content [Br-UGC]). As online consumer behavior does not occur in a cultural void, the present study extends earlier research by explicitly examining how the collectivism-individualism dimension, both at the national and at the personal level, influences consumers’ engagement (“liking,” commenting on, and sharing) with different types of Br-UGC created by different sources. Results based on a diverse sample of participants from South Korea, Thailand, the Netherlands, and the United States (N = 812) suggest that collectivism-individualism at the national level moderates the effects of content characteristics and social relationships on Br-UGC engagement. Moreover, consumers who hold the same values as others in their national culture are more comfortable sharing informative Br-UGC.

De overgang naar een digitale communicatieomgeving heeft de afgelopen tientallen jaren de manier waarop consumenten en merken met elkaar communiceren drastisch veranderd. SWOCC publicatie 77 ‘Automated 1-2-1 Communication’ laat zien hoe merken strategisch gebruik kunnen maken van de conversational agents – zoals chatbots en virtuele assistenten. Het biedt communicatieprofessionals hulp bij de implementatie van geautomatiseerde 1-op-1 communicatie. Uniek aan het onderzoek is dat de bevindingen van eerder wetenschappelijk onderzoek over dit onderwerp zijn gecombineerd met de ervaringen van managers en developers die voor Nederlandse organisaties werken en de inzichten van consumenten. Aan de hand van deze bevindingen worden in de publicatie de volgende vragen beantwoord: Welke kansen liggen er voor de organisatie met geautomatiseerde 1-op-1 communicatie? Welke impact heeft de implementatie van geautomatiseerde 1-op-1 communicatie op medewerkers en de organisatie? Welke overwegingen zijn van belang bij de implementatie van geautomatiseerde 1-op-1 communicatie? Hoe ervaren consumenten geautomatiseerde 1-op-1 communicatie?

Ongoing advances in artificial intelligence (AI) are increasingly part of scientific efforts as well as the public debate and the media agenda, raising hopes and concerns about the impact of automated decision making across different sectors of our society. This topic is receiving increasing attention at both national and cross- national levels. The present report contributes to informing this public debate, providing the results of a survey with 958 participants recruited from high-quality sample of the Dutch population. It provides an overview of public knowledge, perceptions, hopes and concerns about the adoption of AI and ADM across different societal sectors in the Netherlands. This report is part of a research collaboration between the Universities of Amsterdam, Tilburg, Radboud, Utrecht and Eindhoven (TU/e) on automated decision making, and forms input to the groups’ research on fairness in automated decision making. Report

Since news circulation increasingly takes place online, the public has gained the capacity to influence the salience of topics on the agenda, especially when it comes to social media. Considering increased scrutiny about organizations, this study aims to understand what causes heightened activity to organization-related topics among Twitter users. We explore the extent to which news value theory, news coverage, and influential actors can explain peaks in Twitter activity about organizations. Based on a dataset of 1.8 million tweets about 18 organizations, the findings show that the news values social impact, geographical closeness, facticity, as well as certain influential actors, can explain the intensity of online activities. Moreover, the results advocate for a more nuanced understanding of the relation between news media and social media users, as indications of reversed agenda-setting patterns were observed.

Disembodied conversational agents in the form of chatbots are increasingly becoming a reality on social media and messaging applications, and are a particularly pressing topic for service encounters with companies. Adopting an experimental design with actual chatbots powered with current technology, this study explores the extent to which human-like cues such as language style and name, and the framing used to introduce the chatbot to the consumer can influence perceptions about social presence as well as mindful and mindless anthropomorphism. Moreover, this study investigates the relevance of anthropomorphism and social presence to important company-related outcomes, such as attitudes, satisfaction and the emotional connection that consumers feel with the company after interacting with the chatbot.

We present INCA (short for INfrastructure for Content Analysis), a Python module for collecting, storing, processing, and analyzing a wide variety of media content, including but not limited to news, political debates, social media, forums, and customer reviews. Using Elasticsearch as a database backend and Celery for task management, it makes automated content analysis scalable. INCA’s main objective is to enable and promote an integrated workflow. INCA focuses on re-usability of data, processors, and analyses; making all steps of automated content analysis (ACA) accessible to social scientists, without requiring advanced programming skills. Here, we present the aim, implementation and recommended workflow for INCA.

Research investigating the drivers of consumers’ engagement with brands on social media is proliferating. However, little is known about how advertising outside social media drives engagement with brands on social media. This study aims to explore the relation between advertising spend in different offline media (TV, radio, newspapers, magazines, out of home), and reach of and engagement with brand pages on Facebook. The study uses a unique real-life data-set containing information about the Facebook pages of 45 brands for approximately three years combined with Nielsen Advertising Spend data. Results showed that while advertising in offline media influenced organic and viral reach, the number of page likes was directly influenced by advertising on Facebook only. It can be concluded that offline advertising is relevant in driving consumers’ online brand engagement; however, there is a unique set of drivers for organic reach, viral reach and likes.

The increasing volume of images published digitally requires social science and communication researchers to employ methods able to perform visual content analysis at a large scale. Ongoing advances in machine vision and the ability to automatically detect objects, concepts and features in images provide a promising opportunity to address this challenge, yet it is often not feasible for social science researchers to develop their own custom classifier given the volume of images, resources and technical expertise needed. We therefore propose a research protocol with which existing pre-trained (commercial) models can be used for theory-building purposes despite their black box approach.

Given the increased relevance of social networking sites (SNSs) for consumers around the globe, companies face the challenge of understanding motivations underlying consumers’ interactions with online brand-related content. Cross-cultural research on consumer motivations for online brand-related activities on SNSs, however, is limited. The present study explored, via in-depth interviews, reasons why Facebook users from individualistic (the Netherlands, the United States) and collectivistic (South Korea, Thailand) cultures engage with brand-related content. The findings provide in-depth insights, in particular, with regards to collectivistic consumers, to the varied interpretations of the motivations for COBRAs identified in previous literature. We also identified a new motivation specifically for collectivistic cultures: the desire to share an intention to purchase or try a product. Moreover, while collectivistic motivations were driven by the wish to express a sense of belonging to the social group, individualistic cultures appear to engage with brands mainly for obtaining advantages for themselves.

De SWOCC publicatie 73 Corporate Branding and Consumers laat zien dat wat consumenten op social media lezen over een corporate brand mede bepaalt wat zij van het product brand vinden. Zelfs wanneer het geen onderdeel is van de communicatiestrategie van een bedrijf, associëren consumenten het product met de corporate brand. In hoeverre draagt social media hieraan bij? Wat zijn de strategieën die bedrijven in kunnen zetten bij het vertalen van de corporate branding strategie naar een social media beleid? Publicatie 75 Corporate branding and consumers on social media geeft aanbevelingen voor het opzetten van een social media strategie met de corporate brand in gedachten. Gebaseerd op analyses over het social media gedrag van consumenten en bedrijven, wordt antwoord gegeven op de vraag: welke beslissingen dienen bedrijven te nemen bij het opzetten (of heroverwegen) van hun social media activiteiten om het corporate merk optimaal te benutten?

Corporate social responsibility (CSR) communication is becoming increasingly important for brands and companies. Social media such as Twitter may be platforms particularly suited to this topic, given their ability to foster dialogue and content diffusion. The purpose of this paper is to investigate factors driving the effectiveness of CSR communication on Twitter, with a focus on the communication strategies and elements of storytelling. Using a sample of 281,291 tweets from top global companies in the food sector, automated content analysis (including supervised machine learning) was used to investigate the influence of CSR communication, emotion, and aspirational talk on the likelihood that Twitter users will retweet and like tweets from the companies. The findings highlight the importance of aspirational talk and engaging users in CSR messages. Furthermore, the study revealed that the companies and brands on Twitter that tweeted more frequently about CSR were associated with higher overall levels of content diffusion and endorsement. This study provides important insights into key aspects of communicating about CSR issues on social networking sites such as Twitter and makes several practical recommendations for companies.

Given the importance of survey measures of online media use for communication research, it is crucial to assess and improve their quality, in particular because the increasingly fragmented and ubiquitous usage of internet complicates the accuracy of self-reported measures. This study contributes to the discussion regarding the accuracy of self-reported internet use by presenting relevant factors potentially affecting biases of self-reports and testing survey design strategies to improve accuracy. Combining automatic tracking data and survey data from the same participants (N = 690) confirmed low levels of accuracy and tendencies of over-reporting. The analysis revealed biases due to a range of factors associated with the intensity of (actual) internet usage, propensity to multitask, day of reference, and the usage of mobile devices. An anchoring technique could not be proved to reduce inaccuracies of reporting behavior. Several recommendations for research practice follow from these findings.

Using a sample of over 5300 tweets from top global brands, this study investigated how different types of users can influence brand content diffusion via retweets. Twitter users who influenced followers to retweet brand content were categorized as (1) influentials, because of their above average ability to influence others to retweet their tweets (in general), (2) information brokers, because of their position connecting groups of users or (3) having strong ties, because of their high percentage of friends in common and a mutual friend–follower relationship with the influenced follower. The results indicate that influentials and information brokers are associated with larger number of retweets for brand content. In addition, although information brokers have a larger overall influence on retweeting, they are more prone to do so when influentials are mentioned in the brand tweet, providing support for the strategy that aims to associate the brand with influential users.

Het werkveld van de corporate marketeer is complex. Er is niet alleen steeds meer concurrentie. Ook is het moeilijker geworden om een corporate merk te bouwen door de opkomst van social media, het versnipperde medialandschap en de maatschappelijke zorgen over vertrouwen, geloofwaardigheid en transparantie. De keuze voor een succesvolle corporate branding strategie gericht op consumenten is, met alle mogelijkheden die er zijn, dan ook niet gemakkelijk. De SWOCC-publicatie Corporate Branding and Consumers helpt bij de keuze voor de juiste strategie. Het laat zien op welke manier corporate branding relevant kan zijn voor consumenten en geeft handvatten voor de toepassing ervan.

How do certain cues influence pass-along behavior (re-Tweeting) of brand messages on Twitter? Analyzing 19,343 global brand messages over a three-year period, the authors of this article found that informational cues were predictors of higher levels of re-Tweeting, particularly product details and links to a brand’s website, social network sites, and photos or videos. And, although emotional cues did not influence re-Tweeting on their own, they reinforced the effects of informational cues and traceability cues (hashtags) when combined in the same message. In other words, Twitter users especially are interested in messages that are rich in informational content.

This study focuses on how brands participate in social network sites (SNSs) and investigates both the different strategies they adopt and the factors that influence these strategies. The activities of top brands in SNSs were investigated through a content analysis of brand web sites in three countries, measuring two primary dimensions: presence in SNSs and level of engagement which the brand hoped to establish with stakeholders. The study then built logistic regression models to understand which factors drive brands to adopt and use SNSs, using insights from previous studies related to internet innovation adoption cycles. The study confirmed that SNS adoption follows a general path of Internet innovation adoption by brands. Industry markets exert influence, with technology and consumer intensive brands being more likely than all other industry segments to be present in SNSs and to participate at higher engagement levels, while brands targeting younger audiences also engage at higher levels than brands targeting generic audiences. The country in which the brand operates plays a significant role in a brand’s likelihood of adopting SNSs: web sites in the USA are more likely to use SNSs than other countries, even after controlling for factors such as pre-existing web operations or the headquarters being in the same country. It was also found that top brands operate in a global world, as shown by their preference for SNSs with global coverage compared to local SNS, even when the penetration rates of these local SNSs were higher. The paper advances research on SNS adoption by brands from an organizational standpoint, at a time when the majority of the top global brands actively promote such services.

This study aims to contribute to an emerging literature that seeks to understand how identity markers on social networking sites (SNSs) shape interpersonal impressions, and particularly the boundaries that SNSs present for articulating unconstrained ‘hoped-for possible selves.’ An experiment employing mock-up Facebook profiles was conducted, showing that appearing with friends on a Facebook profile picture as well as increasingly higher number of Facebook friends strengthened perceptions of a profiler’s hoped-for level of social connectedness. Excessive numbers of friends, however, weakened perceptions of a profiler’s real-level social connectedness, particularly among participants with smaller social networks on Facebook themselves. The discussion focuses on when people come to find that reasonable boundaries of self-generated information on an SNS have been exceeded.