Media Effects Theories1
An Overview
Patti M. Valkenburg and Mary Beth Oliver
Theories and research on the effects of media emerged under the umbrella concept mass communication. This term arose during the 1920s as a result of the new opportunities to reach audiences via the mass media (McQuail, 2010). In early mass communication theories, mass not only refered to the "massness" of the audience that media could reach but also to homogeneous media use and homogeneous media effects, notions that are increasingly challenged in the contemporary media landscape (Valkenburg, Peter & Walther, 2016). In the past two decades, media use has become progressively individualized, and, with the introduction of Web 2.0, decidedly more personalized. It is no surprise, therefore, that media effects theories have undergone important adjustments in the past decades. And it is also no surprise that the mass has turned increasingly obsolete in contemporary media effects theories (Chaffee & Metzger, 2001).
The aim of this chapter is to provide an overview of the most important media effects theories that have been coined in the past decades and to chart changes in these theories. We start by providing a definition of a media effects theory and explaining the differences between media effects theories and models. In the second section, we discuss the results of several biblio-metric studies that have tried to point out the most prominent media effects theories in central communication journals, and, based on these studies we identify "evergreen" and upcoming theories. In the third section, we discuss the communalities between contemporary media effects theories along three potential characteristics of such theories: selectivity, transactionality, and conditionality. We end with a discussion of the future of media effects research, with a special focus on the necessity of the merger between media effects and computer-mediated communication theories.
What Is a Media Effects Theory?
As Potter (2011) rightly observes in his review of the media effects literature, few scholars have attempted to provide a formal definition of a media effect. We can add to this observation that even fewer scholars have formulated a definition of a media effects theory. Without such
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Media Effects Theories • 17
a definition, it is difficult to assess which theories qualify as media effects theories and which do not. But to be able to document well-cited media effects theories that have been developed over the years, we first and foremost need a definition of a media effects theory. We define such a theory as one that attempts to explain the uses and effects of media on individuals, groups, or societies as a whole. To be labeled a media effects theory, a theory at least needs to conceptualize media use (or exposure to specific mediated messages or stories) and the potential changes that this media use can bring about in individuals, groups, or societies (i.e., the media effect). We define media use broadly as the intended or incidental use of media channels (e.g., telephone, email), devices (e.g., smart-phone, game console), content/messages (e.g., games, narratives, advertising, news), or all types of platforms, tools, or apps (e.g., Facebook, Instagram, Uber). Media effects are the deliberate and non-deliberate short and long-term individual or collective changes in cognitions, emotions, attitudes, and behavior that result from media use (Valkenburg et al., 2016).
Some media effects theories that fit within this definition have previously been labeled as media effects models, oftentimes (but not always) because they are accompanied by a pictorial model to explain the processes or relationships between media use, media outcomes, and other relevant concepts, such as individual differences or social-context variables (e.g., the Elaboration Likelihood Model, Petty & Cacioppo, 1986; the Reinforcing Spiral Model; Slater, 2007). In other scholarly publications, the labels theory and model are used interchangeably. For example, in the previous edition of this book, some authors referred to the agenda setting model (Tewks-bury & Scheufele, 2009, p. 21), whereas others referred to agenda setting theory (McCombs & Reynolds, 2009, p. 13). Although there are many conceptions about the differences between theories and models within and beyond the communication discipline, these conceptions do not seem to be helpful in distinguishing media effects theories from models. In fact, all media effects models that will be discussed in this chapter fit within our definition of media effects theories. Therefore, although we will use the original labels of existing models/theories (e.g., the Elaboration Likelihood Model versus cultivation theory), we will use these labels without distinction.
Prominent Media Effects Theories
In the past 20 years, five bibliometric studies have tried to single out the most prominent media effects theories in scholarly communication work (Bryant & Miron, 2004; Chung, Barnett, Kim & Lackaff, 2013; Kamhawi & Weaver, 2003; Potter, 2012; Walter, Cody & Ball-Rokeach, 2018). These bibliometric studies have content-analyzed a varying number of communication journals to document, within a certain time frame, which theories are most often cited in these journals. For example, Bryant and Miron (2004) analyzed one issue per year from three communication journals (Journal of Communication, Journal of Broadcasting and Electronic Media, and Journalism & Mass Communication Quarterly) from 1956 to 2000, Chung et al. (2013) analyzed all issues from four communication journals from 2000 to 2009 (Journal of Communication, Communication Research, Human Communication Research, and Communication Monographs), and Walter et al. (2018) analyzed all issues from one communication journal (Journal of Communication) from 1951 to 2016.
The bibliometric studies all focused on the prevalence of mass communication theories rather than media effects theories specifically. Although both types of theories are sometimes used interchangeably, the focus of mass communication theories is decidedly broader than that of media effects theories. Generally, mass communication theories do not only conceptualize the effects of mass communication, but also its production, consumption, and distribution, as well
18 • Patti M. Valkenburg and Mary Beth Oliver
as the (changes in) policies surrounding mass communication. For example, in Bryant and Miron's (2004) analysis, mass communication was defined as "any scholarship that examined processes, effects, production, distribution, or consumption of media messages" (p. 663). In addition, whereas mass communication theories have traditionally embraced both postpositivist and critical or cultural approaches (Chaffee & Metzger, 2001), media effects theories are primarily associated with postpositivist approaches. Postpositivists derive their quantitative research methods from those developed in the physical sciences, but they do recognize that humans and human behavior are not as constant and homogeneous as elements in the physical world (Baran & Davis, 2010). Indeed, most chapters in this book rely on theories or discuss research that stem from postpositivist approaches.
Some bibliometric studies did not only analyze (mass) communication theories, but all theories, including those that originated in cognate disciplines. For example, Bryant and Miron identified 604 theories in their analyzed journals, including theories such as feminist theory, attribution theory, and Marxism. Likewise, Potter (2012) found 144 different theories from within and beyond the communication discipline, including theories like the availability heuristic, cognitive dissonance, and self-perception (see also Potter & Riddle, 2007; Walter et al., 2018). According to Potter, these theories all described "some aspect of the media effects phenomenon" (p. 69). However, although all these theories may be helpful to explain media effects, in themselves they cannot be considered media effects theories as defined in this chapter. As discussed, a media effects theory at least needs to conceptualize media use and the individual or collective changes that this media use brings about.
Despite the fact that the bibliometric studies used different classifications of communication theories and analyzed different communication journals, together they provide an indispensable picture of the use and development of media effects theories in the past decades. Because media effects theories did play such a dominant role in all bibliometric studies (Chung et al., 2013), we were able to reanalyze the results of these studies with an exclusive focus on the media effects theories that they identified. For example, of the 144 theories that Potter (2012) identified, about one-fifth qualify as media effects theories according to our definition.
Table 2.1 lists the media effects theories that have been identified as most prevalent in the bibliometric studies. In ranking these theories, we opted to include the 1956-2000 period reported by Bryant and Miron (2004) and the most recent years (2010-2016) from Walter et al.'s (2018) study so as to provide a picture of changes and trends within the discipline. However, in listing these theories, it is important to note that their ranking should be understood in general terms rather than as necessarily representing stark or significant differences. First, some of the theories listed were "tied" in terms of their frequencies. For example, in Bryant and Miron's (2004) analysis, agenda setting and uses and gratifications had 61 citations each, and medium dependency and linear theory had 16 citations each; in Kamhawi and Weaver's (2003) analysis, priming and knowledge gap theory were mentioned in fewer than 1.5% of the articles sampled. Second, even when theories differed in terms of their prevalence, some of these differences are so small as to warrant caution in their interpretation. For example, in Chung et al.'s (2013) analysis, cultivation theory was associated with 68 mentions, and agenda setting was associated with 65 mentions. Finally, in some analyses, different theories were sometimes grouped together with similar theories in a common category, thereby increasing their prominence in the rankings. For example, in Walter et al.'s (2018) study, the "narrative theory" was employed to refer to articles that employed theories or concepts such as transportation, entertainment education, and character identification.
Media Effects Theories • 19
Table 2.1 Prominent Media Effect Theories Listed in Five Bibliometric Studies to Document Communication Theories
Study	Bryant and	Kamhawi and	Potter (2012)	Chung et al.	Walter et al.
	Miron (2004)	Weaver (2003)		(2013)	(2018)
Period	1956-2000	1980-1999	1993-2005	2000-2009	2010-2016
Journals (n)	3 comm. journals	10 comm.	13 comm.	4 comm.	1 comm. journal
		journals	journals; 3 other	journals	
			journals		
Articles (n)	1,806	889	8,855	1,156	294
Top theories	I, Agenda setting	1. Information	1. Cultivation	1. Framing	1. Framing
	(tied)	processing	theory	theory	theory
		models (e.g.,			
		limited			
		capacity			
		model)			
	1. Uses and	2. Uses and	2. Third-person	2. Priming	2. (Narrative)
	gratifications	gratifications	effect	theory	entertainment
	(tied)				theories
	2. Cultivation	3. Cultivation	3. Agenda	3. Cultivation	3. Agenda
	theory	theory	setting	theory	setting
	3. Social learning	4. Agenda	4. Uses and	4. Agenda	4. Selective
	theory	setting	gratifications	setting	exposure
					theory
	4. Diffusion of	5. Diffusion of	5. Priming	5. Elaboration	5. Dual
	innovations	innovations	theory	Likelihood	processing
	theory	theory		Model	models (e.g.,
					ELM)
	5. McLuhan's	6. Framing	6. Limited	6. Third-person	6. Priming
	medium theory	theory	capacity	effect	theory
			model		
	6. Medium	7. Medium	7. Framing	7. Social	7. Uses and
	dependency	dependency	theory	cognitive	gratifications
	(tied)	theory		theory	
	6. Linear theory	8. Priming	8. Social	8. Diffusions of	8. Social
	(tied)	theory (tied)	cognitive	innovations	cognitive
			theory	theory	theory
	7. Laswell's	8. Knowledge	9. Elaboration	9. Theory of	9. Mood
	communication	gap (tied)	Likelihood	reasoned	management
	model		Model	action	theory/Hostile
					media effect
20 • Patti M. Valkenburg and Mary Beth Oliver
Table 2.2 Prominent Media Effects Theories and Their Google Citations
Author(s)	Theory/Model	Citations	Description
Lazarsfeld et al.	Two-step flow	9,783	Argues that media effects are indirect rather than
(1948)	theory		direct and established through the personal influence
			of opinion leaders.
Rogers (1962)	Diffusion of	94,813	Explains how, why, and at what rate new ideas and
	innovations		technology spread among participants in a social
			system.
Gerbner, 1969	Cultivation theory	574	Argues that the more time people spend 'living' in the
			television world, the more likely they are to believe
			the social reality portrayed on television.
Tichenor et al.	Knowledge gap	2,049	Discusses how mass media can increase the gap in
(1970)	theory		knowledge between those of higher and lower
			socioeconomic status.
McCombs and	Agenda setting	10,181	Describes how news media can influence the salience
Shaw (1972)	theory		of topics on the public agenda.
Katz et al. (1973)/	Uses and	2,277/	Attempts to understand why and how people actively
Rosengren (1974)	gratifications theory	719	seek out specific media to satisfy specific needs.
Noelle-Neumann	Spiral of silence	1,696	Discusses people's tendency to remain silent when
(1974)	theory		their views differ from the majority view. Media
			contribute to the development of majority views.
Ball-Rokeach and	Media system	1,173	Argues that the more a person depends on media to
DeFleur(1976)	dependency theory		meet needs, the more important media will be in
			a person's life, and the more effects media will have.
Bandura(1977,	Social learning/	47,049/	Analyzes the mechanisms through which symbolic
2009)	social cognitive	3,878	communication through mass media influences
	theory		human thought, affect, and behavior.
Berkowitz (1984)	Priming theory	875	Argues that media can activate cognitions and related
			affect/behaviors stored in human memory.
Davison (1983)	Third-person effect	1,875	Predicts that people tend to believe that media
			messages have a greater effect on others than on
			themselves.
Petty and	Elaboration	9,089	Explains how mediated stimuli are processed (via
Cacioppo (1986)	Likelihood Model		either the central or peripheral route), and how this
			processing influences attitude formation or change.
Entman (1993)/	Framing/	11,965/	Discusses how the media draw attention to certain
Scheufele (1999)	Framing as a theory	3,816	topics and place them within a field of meaning (i.e.,
	of media effects		frame), which in turn influences audience
			perceptions.
Lang, Dhillon and	Limited capacity	279/	Analyzes how people's limited capacity for
Dong (1995)/	model	1,522	information processing affects their memory of, and
Lang, (2000)			engagement with, mediated messages.
Media Effects Theories • 21
Evergreen Media Effects Theories
As Table 2.1 reveals, six media effects theories have held up fairly well over the past decades, and so they can rightly be named "evergreen theories." These theories showed up as top-cited theories in both the earliest bibliometric study (time frame 1956-2000; Bryant & Miron, 2004), and in two to four bibliometric studies that covered subsequent periods: cultivation theory (Gerbner, 1969), agenda setting theory (McCombs & Shaw, 1972), diffusion of innovations theory (Rogers, 1962), uses and gratifications theory (Katz, Blumler & Gurevitch, 1973; Roseng-ren, 1974), social learning/social cognitive theory (1986), and media system dependency theory (Ball-Rokeach & DeFleur, 1976).
Other theories that were identified as well-cited theories in the bibliometric studies are two-step flow theory (Lazarsfeld, Berelson & Gaudet, 1948), knowledge gap theory (Tichenor, Dono-hue & Olien, 1970), spiral of silence theory (Noelle-Neumann, 1974), priming theory (Berkowitz, 1984), third-person effects (Davison, 1983), the Elaboration Likelihood Model (Petty & Cacioppo, 1986), framing theory (Entman, 1993), and the limited capacity model (Lang, 2000). Table 2.2 gives a short description of the well-cited media effects theories identified in the bibliometric studies, listed according to the dates in which they were originally coined.
Changes in the Prominence of Theories over Time
When comparing the results of the five bibliometric studies summarized in Table 2.1, some theories appear to have lost their appeal over the years. One such theory is Lasswell's (1948) model of communication that was listed as one of the top-cited theories in Bryant and Miron's (2004) analysis but lost that status in the more recent bibliometric studies. The same holds for other classic, linear media effects models, such as Shannon and Weaver's (1949) mathematical model of communication. Another theory that was present in Bryant and Miron, but which lost its influence after the 1970s, is McLuhan's medium (or sense-extension) theory (McLuhan, 1964). By means of his aphorism, "the medium is the message," McLuhan theorized that media exert their influence primarily by their modalities (e.g., text, aural, audiovisual) and not so much by the content they deliver. His theory probably lost its appeal among media effects researchers because research inspired by his theory often failed to produce convincing results (Clark, 2012; Valkenburg et al., 2016). Although no one can deny that modality is an essential feature of media and technologies (Sundar & Limperos, 2013), media effects are often a result of a combination of features, among which content plays a prominent role. It is probably no surprise that "Content is King" is still one of the more popular adages in modern marketing.
Another change over time suggested by the bibliometric studies is the "cognitive turn" in media effects theories coined in the 1980s and 1990s. This increased attention to internal cognitive processes of media users is at least in part a result of the cognitive revolution in psychology that started in the 1950s in reaction to behaviorism (Gardner, 1985). Behaviorism (or stimulus-response theory) is a learning theory that argues that all human behaviors are involuntary responses to rewarding and punishing stimuli in the environment. What happens in the mind during exposure to these stimuli is a "black box" and is irrelevant to study.
In the 1980s and 1990s, several media effects theories have tried to open the black box between media use and media outcomes (e.g., priming theory, Berkowitz, 1984; the limited capacity model, Lang et al., 1995; the Elaboration Likelihood Model, Petty & Cacioppo, 1986). At the time, scholars started to acknowledge that in order to validly assess whether (or not) media
22 • Patti M. Valkenburg and Mary Beth Oliver
can influence individuals, they need to know why and how this happens. This new generation of theories acknowledged that media effects are indirect (rather than direct). More specifically, they argued that the cognitive mental states of the viewer act as a mediating (or intervening) variable between media use and media outcomes. Indeed, these new theories recognized that the mental states of the media user play a crucial role in explaining media effects.
In the same period, some classic media effects theories were adjusted to better acknowledge cognitions in the media effects process, sometimes by the author him or herself and sometimes by others. For example, in Bryant and Miron's bibliometric study, Bandura's theory was still named social learning theory (Bandura, 1977). This early version of his theory had its roots in behaviorism, which is evident, for example, from its unconditional emphasis on rewarding and punishing stimuli to realize behavioral change. In the 1980s, Bandura modified his theory and renamed it social cognitive theory to better describe how internal cognitive processes can increase or decrease learning (Bandura, 1986). In addition, although cultivation theory is an all-time favorite and its name is still current, over the past few decades researchers have proposed numerous adaptations to the theory to better understand how, why, and when cultivation effects occur. For example, Shrum (1995) has argued for the integration of cultivation theory in a cognitive information processing framework According to Potter (2014), the adaptations of cultivation theory are so numerous and extensive that its original set of propositions may have gotten glossed over. Indeed, there appears to be only minimal overlap between the macro-level, sociological cultivation theory that Gerbner (1969) proposed and the more recent micro-level, psychological interpretations of the same theory (Ewoldsen, 2017; Potter, 2014).
Upcoming Media Effects Theories
Although highly informative, together the five bibliometric studies either do not (Bryant 8c Miron, 2004; Kamhawi & Weaver, 2003; Potter, 2012) or only partly cover the past decade of media effects research (Chung et al., 2013; Walter et al., 2018). The most recent study by Walter et al. (2018) does cover publications that appeared up to 2016. But due to their study's broader scope, they only focused on research papers and omitted theoretical papers from their analysis, whereas these latter papers typically are the ones in which new media effects theories are coined. Given the rapid changes in media technologies in the past decade, it is highly relevant to investigate whether this recent period has witnessed an upsurge in novel or adjusted media effects theories. After all, as media technologies change, "new theories may be needed with which to understand the communication dynamics that these technologies involve" (Wal-ther, Van Der Heide, Hamel & Shulman, 2009, p. 230).
To identify upcoming media effects theories, we conducted an additional bibliometric analysis, in which we included the same 14 communication journals as the most extensive earlier analysis did (Potter, 2012; see Potter & Riddle, 2007). To capture theories and research that are particularly relevant to newer communication technologies, we included an additional communication journal: the Journal of Computer Mediated Communication. To identify highly cited articles in these 15 journals, we used the "highly cited paper" option provided by the citation indexing service Web of Science (WoS). Highly cited papers in WoS reflect articles in the last ten years that were ranked in the top 1% within the same field of research (e.g., communication) and published in the same year (Clarivate Analytics, 2017). An advantage of this analysis is that, within the designated ten-year period, older and recent papers are treated equally. Whereas in regular citation analyses older papers typically outperform more recent ones, the algorithm of WoS controls for this "seniority bias."
Media Effects Theories • 23
Our analysis yielded 93 highly cited papers in these 15 journals.2 Of these papers, about half involved media effects papers, which underscores the relevance of media effects research in the communication discipline. Most of these effects papers were empirical papers that used one or more existing theories to guide their research. However, a small percentage (about 10%) either introduced a new media effects theory or extended one or more existing theories. Some of these theoretical papers focused on media use in general (e.g., the reinforcing spiral model, Slater, 2007; the Differential Susceptibility Model of Media Effects, Valkenburg & Peter, 2013). Others dealt with specific types of media use, such as exposure to news (e.g., framing theory, Entman, 2007; the communication mediation model, Shah et al., 2017), persuasive messages (e.g., the model of psychological reactance to persuasive messages, Rains, 2013), or communication technology (extensions of spiral of silence theory and two-step flow theory, Neubaum & Krämer, 2017; the uses and gratifications theory 2.0, Sundar & Limperos, 2013).
A first noticeable trend revealed by the highly cited media effects papers is the emergence of theories that attempt to explain the uses and effects of media entertainment (for a similar observation, see Walter et al., 2018; Table 2.1). Some of these theories try to better understand this type of media use by focusing on cognitive and emotional processing. They try to explain, for example, why and how exposure to narrative entertainment leads to less resistance than traditional persuasive messages (the entertainment overcoming resistance model, Moyer-Guse, 2008; Moyer-Guse 8c Nabi, 2010). Other theories have tried to better understand the concept of enjoyment in response to media entertainment (Tamborini, Bowman, Eden, Grizzard 8c Organ, 2010), or the "eudaimonic gratifications" (i.e., media-related experiences associated with contemplation and meaningfulness) that people experience in response to thought-provoking and poignant entertainment (Oliver 8c Bartsch, 2010; Oliver 8c Raney, 2011).
Another trend that can be inferred from the highly cited media effect studies is that the traditional gap between media effects and CMC (Computer-Mediated-Communication) studies seems to have narrowed somewhat in the past years. Traditionally, "media effects research" and "CMC research" were part of two subdisciplines of communication science that developed in separation and rarely interacted with each other. Media effects research was part of the mass communication subdiscipline, whereas CMC research belonged to the interpersonal communication subdiscipline. Over time, many authors have argued for bridging the gap between these two subdisciplines, oftentimes without much success (for a review see Walther 8c Valkenburg, 2017).
However, the significant changes in media use in the past decade seemingly have been an important impetus for the merger between media effects and CMC theories. After all, whereas previously "media use" referred only to a handful of mass media such as newspapers, radio, film, and television, the current definition of media use, including the one in this chapter, also includes an array of media technologies that stimulate give-and-take interactions of individuals or groups with technologies (e.g., games) or other individuals (e.g., social media) and that traditionally belonged to "the realm" of CMC theories and research.
In fact, several CMC studies in our collection of highly cited papers did investigate "media effects" that fall within our definition of such effects. For example, Walther, Van der Heide, Kim, Westerman and Tong (2008) found that CMC users' perceptions of an individual's online profile are affected by the posts of friends who may have posted on the profile. We consider such a scenario as an example of a media effect. Namely, people (i.e., the receivers) look at online profiles (i.e., media use), and the messages or posts that they see (i.e., the messages) affect their perceptions (i.e., the media effect). Similarly, Tong, Van Der Heide, Langwell and Walther (2008) investigated how exposure to the number of friends listed on online profiles
24 • Patti M. Valkenburg and Mary Beth Oliver
(i.e., media use) influenced observers' perceptions of these profiles (i.e., the media effect). Their study showed that this system-produced information significantly influenced the cognitions and attitudes of the receivers of these messages.
Core Features of Contemporary Media Effects Theories
The previous section revealed several changes in media effects theories over the past decades, such as the cognitive turn in these theories as of the 1980s and 1990s, the emphasis on media entertainment and emotional media processing, and the gradual integration of media effects and CMC research. Generally, the more recent theories appear to be more comprehensive than earlier ones. For example, they more often recognize the interaction between media factors (media use, media processing) and non-media factors (e.g., dispositional, situational, and social context factors), and they better acknowledge that media effects are indirect rather than direct. In the next sections, we discuss how contemporary media effects theories differ from the earlier ones. We focus on three related core features of these theories: selectivity, transactionality, and conditionality.
Selectivity Paradigm
Selectivity is one of the oldest paradigms in communication. Already in the 1940s, Lazarsfeld et al. (1948) discovered that individuals predominandy select media messages that serve their needs, goals, and beliefs. These early ideas have been further conceptualized into two theories: the uses and gratifications (Katz et al., 1973; Rosengren, 1974) and selective exposure theory (Knobloch-Westerwick, 2014). Both theories are generally based on three propositions: (1) individuals only attend to a limited number of messages out of the miscellany of messages that can potentially attract their attention; (2) media use is a result of dispositional (e.g., needs, personality), situational (e.g., mood), or social-context factors (e.g., the norms that prevail in the social environment); and (3) only those messages they select have the potential to influence them (Klapper, 1960). This influence of media use is named "obtained gratifications" in uses and gratifications theory and "media effects" in selective exposure theory.
Early empirical research guided by uses and gratifications and selective exposure theory usually investigated only the first part of the media effects process. This research typically conceptualized media use as the outcome, whereas the consequences or "effects" of this media use were typically ignored. Therefore, these early theories do not fit within our definition of media effects theories. In the past decade, however, the selectivity paradigm has progressively become an integrated part of media effects theories, including the reinforcing spiral model (Slater, 2007); the SESAM model (Knobloch-Westerwick, 2014; see Chapter 10 in this volume) and the Differential Susceptibility to Media Effects Model (Valkenburg & Peter, 2013). Indeed, in Walter et al.'s (2018) bibliometric analysis, selective exposure appeared as a top theory only in the last time frame examined (2000-2016). Contemporary selective exposure theories conceptualize that media users, rather than media sources, are the center points in a process that may bring about media effects. This insight has important implications for media effects research. It means, for example, that individuals, by shaping their own selective media use, also (deliberately or not) partly shape their own media effects (Valkenburg et al., 2016).
The selectivity paradigm is also part and parcel of CMC theories and research. For example, Walther, Tong, DeAndrea, Carr and Van Der Heide (2011) argue that the specific goal(s) that
Media Effects Theories • 25
prompt an individual's media consumption "shape attention to variations in the content and features of the topical information one consumes, affecting its interpretation and recall" (p. 187). However, although selectivity is clearly an important feature of CMC, it is still unknown whether CMC users are more (or less) able to contribute to their own media effects than users of more traditional media are. On the one hand, CMC users have more agency in their media selection than they had with traditional media. They can, for example, openly comment on incoming messages, thereby publicly discounting this information. They can also more easily avoid incongruent or conflicting messages, and, due to technological algorithms that use their preferences or search terms, co-create their own "filter bubbles" (Pariser, 2011). Due to this increased agency and selectivity, CMC users may thus have more opportunity than traditional media users to shape their own media effects.
On the other hand, the blending of mass (e.g., a television program) and interpersonal messages (e.g., viewer comments on Twitter about this television program) in CMC environments could also stimulate a type of gratification (or effect) that have been named "process gratifications" (Stafford, Stafford & Schkade, 2004). Unlike content gratifications, process gratifications (or effects) are not so much driven by preexisting needs, goals, or beliefs of the media user, but they develop while using media. For example, individuals may start surfing the web with specific a priori needs, beliefs, or goals, but while interacting with technologies or other people they may develop different and unforeseen needs, goals, and beliefs, which in turn may lead to different and unforeseen media gratifications (or effects). Therefore, in contemporary media effects theories, media effects can best be understood as the result of an interaction between need-driven media use and situational, process-based media use (for a further discussion, see Sundar & Limperos, 2013).
Transactionality Paradigm
In the early days of the communication discipline, most mass media effects theories were linear, one-directional models of communication that pointed from senders (mass media) to receivers. Examples of linear media effects theories are cultivation theory (Gerbner, 1969), Lasswell's (1948) communication model, and McLuhan's (1964) medium theory (see the first column of Table 2.1). Unlike one-directional media effects theories, transactional theories conceptualize media use and media outcomes as reciprocally related. Like uses and gratifications and selective exposure theory, transactional media effects models embrace a user-oriented approach (e.g., Wang & Tchernev, 2012). They argue that (1) certain dispositions of media users (e.g., needs, goals, beliefs) can cause their selective media use; (2) which can, in turn, cause certain outcomes (i.e., the media effect); (3) which can, then, further cause selective media use. For example, adolescents' aggressiveness may stimulate their use of violent media, which, in turn, may increase their aggressiveness, which may then further stimulate their violent media use (Slater, Henry, Swaim & Anderson, 2003).
Transactional media effects theories are relatively recent in the communication discipline. The first transactional media effects model appeared in the early 1980s in Germany (Früh 8t Schönbach, 1982), but that model probably suffered from the rule of the restrictive head start. Transactional models are difficult to investigate and, at the time, both the expertise and the methods to empirically test such complex models were not widely available then. Subsequent transactional media effects models are Bandura's (1986) social cognitive model, Anderson and Bushman's (2002) General Aggression Model, and Slater's (2007) reinforcing spiral model.
26 • Patti M. Valkenburg and Mary Beth Oliver
Although transactionality is relatively new to media effects theories, it has always been a core paradigm of interpersonal communication theories, which, par excellence, attempt to explain the reciprocal influences from interaction partners on one another. However, interpersonal communication has been increasingly mediated through CMC devices. Moreover, in newer media environments, many traditionally one-directional mass communication processes, such as news and entertainment consumption, have become transactional: Message producers and consumers can now exert reciprocal influences on one another and can easily switch their roles from consumers to producers and vice versa. These transactional processes necessitate alterations to existing media effects theories. Such alterations have already been suggested, for example, for agenda setting theory (Lee & Tandoc, 2017), spiral of silence theory (Neubaum & Krämer, 2017), communication mediation theory (Shah et al., 2017), diffusion of innovations theory (Rice, 2017), and entertainment theory (Raney & Ji, 2017).
Conditionality Paradigm
Like the transactionality paradigm, the conditionality paradigm elaborates on the uses and gratifications and selective exposure theories. It postulates that media effects do not equally hold for all media users, and that media effects can be contingent on dispositional, situational, and social-context factors. Remarkably, already in the 1930s, the first large-scale empirical studies into the effects of media on children and young adults, the Payne Fund Studies, concluded:
That the movies exert an influence there can be no doubt. But it is our opinion that this influence is specific for a given child and a given movie. The same picture may influence different children in distinctly opposite directions. Thus in a general survey such as we have made, the net effect appears small.
(Charters, 1933, p. 16)
However, despite these early empirical findings, many subsequent media effects theorists have been rather slow in acknowledging conditional media effects. Particularly early theories aimed at establishing linear, across-the-board effects of mass media. For example, although Gerbner's (1969) cultivation theory did recognize that individuals differ in their interpretation of messages, it did not conceptualize such differences, but instead focused on the macro-level effects of mass-mediated message systems on the public (Potter, 2014). And even today, there seems to be a tendency to ignore individual differences in susceptibility to media effects. As Neuman (2018) recently observed: "Perhaps our paradigm would be strengthened if we recognized that media effects are neither characteristically strong nor are they characteristically minimal: they are characteristically highly variable" (Neuman, 2018, p. 370; see also Rains, Levine & Weber, 2018).
However, despite Neuman's (2018) recent criticism, in fact, most contemporary media effects theories do recognize conditional media effects, including the reinforcing spiral model (Slater, 2007), the communication mediation model (Shah et al., 2007, 2017), and the Elaboration Likelihood Model (Petty & Cacioppo, 1986). Most of these theories have proposed that conditional media effects are not only due to selective exposure but also to selective processing. For example, Valkenburg and Peter (2013) argue that dispositional, situational, and social context factors may have a double role in the media effects process: They not only predict selective exposure, but they can also influence the way in which media content is
Media Effects Theories • 27
cognitively and emotionally processed. Individuals have the tendency, at least to a certain extent, to seek out content that does not deviate too much from their needs, goals, and beliefs (Knobloch-Westerwick, 2014). It is conceivable that the same factors that predict selective exposure can also influence the way in which media content is processed. It has been shown that people's opinions on a given issue influence how they respond to media messages and characters. For example, in their now-classic study about the American series All in the Family, Vidmar and Rokeach (1974) found that high prejudiced viewers tended to be more sympathetic to Archie, the bigoted main character, whereas low-prejudiced individuals tended to be more sympathetic to Mike, the politically liberal main character of the series.
Unfortunately, although in the past decades there has been ample research on selective exposure and selective recall, there has been relatively less attention to selective reception processes (Hart et al., 2009). Moreover, the scarce research that did focus on selective reception has mainly focused on individual differences in cognitive processing of media content and less on emotional processing. However, as our analysis of recent highly cited communication papers suggests, two decades after the cognitive turn in media effects theories, an emotional turn in these theories seems to have unfolded. Indeed, contemporary media effects theories increasingly recognize that emotional processes, such as identification with characters or emotional involvement in the narrative, are important routes to media effects (Moyer-Guse & Nabi, 2010; Nabi, 2009; Slater & Rouner, 2002).
Discussion
Together, the five bibliometric studies that we attempted to integrate in this chapter and our highly cited paper analysis suggest that the use of theory in communication papers has increased significantly across time. For example, whereas Bryant and Miron (2004), who reported on the period from 1956 to 2000, found that only 26% of articles provided a theoretical framework, Potter and Riddle (2007), who reported on the period from 1993 to 2005, found that 35% of articles featured a theory prominently. Finally, Walter et al. (2018) observed that whereas in the 1950s only 9% of all empirical papers that appeared in the journal of Communication featured a theory prominently, this percentage increased towards 65% in the 2010s.
Although it is promising that the development of theory in communication journals has quantitatively increased over the years, it is even more important to establish whether it has improved in a qualitative sense. Some of the bibliometric studies are pessimistic about this qualitative development. For example, Walter et al. (2018) observed a "remarkable slowdown in new theory development" (p. 424) and "a general increase in theory use, yet a decrease in theory development" (p. 435). It must be noted, though, that Walter et al.'s analysis did not include theoretical articles and literature reviews in their bibliometric analysis, which together comprised 11% of their sample of papers. Their conclusions about the state of the field would undoubtedly have been more positive if they had included theoretical papers in their sample.
Walter et al. (2018) based their conclusion on the fact that a number of theories, such as cultivation theory, social cognitive theory, and agenda setting theory, which we dubbed as evergreen theories, remained prominent in every decade after the 1970s. Several other authors have also observed that some theories have been used over and over again up until the present day (Ewoldsen, 2017; Katz & Fialkoff, 2017; Potter, 2014). One explanation for this phenomenon
28 • Patti M. Valkenburg and Mary Beth Oliver
may be that these theories have managed to become part of the shared identity of media effects researchers, who, by referring to or adjusting these theories in their work, are able to communicate this identity. Another explanation may be the high "tolerance" of evergreen theories for multiple interpretations of their claims. Social cognitive theory, for example, is a comprehensive theory with broad concepts that are related to one another in complex ways. An unforeseen consequence of such theories is that they allow researchers to freely interpret or select parts of the theory to justify or explain their results.
Some authors fear that the recurrent referral to these theories distorts what the theory originally proposed (Potter, 2014) or hides the progress that has been made in the understanding of media effects theories (Ewoldsen, 2017). Others have proposed the "retirement" of these old theories and replace them with newer ones that better explain contemporary media use and effects (Katz & Fialkoff, 2017). Indeed, we agree that it is important for the progress of the media effects field to develop new theories with new names rather than to selectively use claims of old theories to justify or explain expected or unexpected results. After all, true theoretical progress can only occur if certain claims of theories that do not hold are formally falsified. Despite the concerns of some authors about the progress in the media effects field, our analysis of recent highly cited communication papers suggests a somewhat more optimistic view. We found that about 10% of the highly cited papers in 15 communication journals published between 2007 and 2017 either introduced a new theory or significantly extended an existing one. These extensions of old theories, such as spiral of silence and diffusion of innovations, were partly due to the rapid changes in the new media landscape, which demands a rethinking of theories that originated in periods when the relation between media and audiences was predominantly anonymous and one-directional.
In this chapter, we summarized several important theoretical trends over the past decade. One such trend is the development of theories that attempt to understand the effects of (narrative) media entertainment and the role of emotional processing in these effects. Another trend is that theories that were coined or extended in the past decade increasingly recognize the selectivity, conditionality, and transactionality of media effects. Finally, despite concerns about the lack of integration between mass and interpersonal communication, we did observe an increased tendency to merge media effects, interpersonal, and CMC theories in papers that investigate the uses and effects of messages communication via the internet and social media.
Challenges and Opportunities for Future Media Effects Research
We are encouraged by the development of media effects theories revealed in our analysis, and we look forward to the new theory development that will undoubtedly evolve in our changing media landscape, where most technologies are simultaneously rapidly new and rapidly old. Both the proliferation of new media technologies and the possibilities to instantaneously interact with other media users pose important challenges and opportunities for future researchers.
Conceptualizing "Media Use 2.0."
First, we anticipate that newer theory development must confront how best to conceptualize what constitutes "media use." Whereas foundational theories often used sweeping measures such as hours-a-day spent with television (e.g., Gerbner, 1969), newer theories need to account for a seemingly endless array of media platforms, even when focusing on a single "type" of
Media Effects Theories • 29
media such as social networking sites. Moreover, given the mobility and multiplicity of media channels, the prevalence of media multitasking has reached new heights, and particularly among younger individuals (Voorveld & van der Goot, 2013). Consequently, watching a favorite television program may now also simultaneously involve chatting with other viewers on fan sites, posting one's reactions to the program on social media, or searching online for information about the actors.
Finally, evolving technologies facilitate media "use" well beyond the time boundaries of any single instance of media consumption. For example, although an individual may watch a given television program for a span of an hour, she may continue to "watch" the show for much longer by engaging with other viewers about the show, by watching replays and commentaries about the show on YouTube, or even expressing her thoughts about the program through self-generated media content such as mashups or fan fiction (Shade, Kornfield & Oliver, 2015). These examples are but a handful of the many ways that media use is changing, thereby highlighting the need to revise or develop new ways to conceptualize and measure how individuals now "use" media content and technology.
New Methods to Assess Cognitive and Emotional Media Processing
Related to the need to reassess how to measure media use, media effects theories may stand to benefit from the evolving use of newer means of assessing individuals' emotional and cognitive processing of messages and resultant changes in beliefs, attitudes, affective states, and behaviors. Whereas traditional scholarship has typically relied on self-reports for studying media effects, many researchers are now turning to alternative techniques. For example, an increasing number of scholars are now examining the neural patterns associated with media use, pointing out its relevance in a host of areas including persuasion, stereotyping, health, and social interaction (see, for example, Falk & Scholz, 2018; Weber, Eden, Huskey, Mangus & Falk, 2015). Likewise, devices such as face readers and eye trackers are currently providing ample opportunity to assess changes in emotional responses to media messages and devices (e.g., Jennett et al., 2008; McDuff, Kaliouby & Picard, 2012; Russell, Russell, Morales 8c Lehu, 2017). Such measurement holds the promise of helping us develop theories about changes in emotions that occur during media use and what such changes imply for resultant media outcomes (Nabi 8c Green, 2014). Further, the opportunity to scrape and analyze big data and networks of information sharing will open many new avenues for media effects theorizing. Although numerous theoretical perspectives have acknowledged the sharing of media messages among individuals (e.g., two-step flow model, diffusion of innovations), network analysis of online communities represents ample opportunities to develop new or adjust existing communication theories.
The Effects of "Mass Self-Communication"
Finally, we eagerly anticipate the growth of media theory that grapples with the implications of the shift from mass communication to what O'Sullivan (2005) has named "masspersonal" and Castells (2007) "mass self-communication." In traditional mass media effects theories, the influence process is unidirectional, from one generator of messages to recipients. Mass self-communication theories provide an extension to these theories, in that they do not only focus on the effects of messages on recipients but also on the effects of those messages on the generator him or herself. The effects of self-generated and self-modified media messages on the message generators themselves have
30 • Patti M. Valkenburg and Mary Beth Oliver
garnered increasing interest among scholars with the emergence of interactive technologies. For example, research on the Proteus Effect demonstrates that people often adopt the characteristics of the avatars that they use to present themselves online (Yee & Bailenson, 2007). Likewise, research on customization of websites and user-interfaces shows that when individuals have the opportunity to select their own digitized environments (e.g., interests, colors, banners), they tend to feel greater affiliation for the environments and heightened perceptions of relevance and interactivity (e.g., Kalyanaraman & Sundar, 2006).
Most recently, Valkenburg (2017) coined the phrase "self-effects" to refer broadly to the effects of messages on the cognitions, emotions, attitudes, and behaviors of the message generators themselves. She argued that in the context of social media, expressing an attitude, stating one's opinion, or even selecting an avatar with a particular appearance may not only influence the cognitions, beliefs, and attitudes of message recipients, but also those of the message generators. Further, as discussed, given individuals' tendencies to select media content that is congruent with their cognitions, beliefs, and attitudes, it is likely that messages which are self-generated and originate from their generator's own beliefs may have an even stronger effect on the message generators themselves than on their message recipients. There is an apparent need for future communication research that investigates and compares the effects and effectiveness of messages on both recipients and message generators themselves.
Conclusion
In sum, our review of media effects theories leads us to end on an optimistic note. Whereas some reviews may suggest that our scholarship is somewhat slow to evolve, our review of media effects theories is heartening. Some theories have remained evergreen, and likely for good reason. Although some of these evergreen theories were developed in what may seem like a long-ago past, their fundamental assumptions about media and human psychology are likely applicable across a wide acreage of media landscapes. At the same time, newer theories, concepts, and foci are populating our scholarship, and reflecting a greater nuance of human experience and of its intersection with communication technologies. Undoubtedly, media effects as a focus of study is at the center of public discourse about interpersonal interaction, political exchange, and even the striving for well-being. We await the insights that will certainly arise from scholars who work toward our understanding of media in the emotional, cognitive, and behavioral lives of its consumers and creators.
Notes
1 Some parts of this chapter are based on Valkenburg et al. (2016). This mostly holds for the section about the three core features of media effects theories, and Table 2.2, which offers an extension and update of a table that appeared in Valkenburg et al. (2016).
2 The list of highly cited articles in these journals can be obtained from Patti Valkenburg: p.m.valkenburg@uva.nl. Two out of the 14 journals that Potter (2012) analyzed (the Quarterly Journal of Speech and the Mass Communication Review) are not indexed in WoS; as a result, no highly cited papers from these journals could be included in our analysis.
References
Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review of Psychology, 53, 27-51. doi: 10.1146/annurev.psych.53.100901.135231
Media Effects Theories «31
Ball-Rokeach, S. }., & DeFIeur, M. L. (1976). A dependency model of mass-media effects. Communication Research, 3(1), 3-21. doi: 10.1177/009365027600300101
Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (2009). Social cognitive theory of mass communication. In J. Bryant & M. B. Oliver
(Eds.), Media effects: Advances in theory and research (pp. 94-124). New York, NJ: Routledge. Baran, S. J., & Davis, D. K. (2010). Mass communication theory: Foundations, ferment, and future.
Boston, MA: Cengage Learning. Berkowitz, L. (1984). Some effects of thoughts on anti- and prosocial influences of media events:
A cognitive-neoassociation analysis. Psychological Bulletin, 95, 410-427. doi:10.1037//0033-
2909.95.3.410
Bryant, J., & Miron, D. (2004). Theory and research in mass communication. Journal of Communication, 54, 662-704. doi:10.1111/j.l460-2466.2004.tb02650.x
Castells, M. (2007). Communication, power and counter-power in the network society. International Journal of Communication, 1, 238-266. doi.T932-8036/20070238
Chaffee, S. H., 8c Metzger, M. J. (2001). The end of mass communication? Mass Communication and Society, 4, 365-379. doi:10.1207/S15327825MCS0404_3
Charters, W. W. (1933). Motion pictures and youth: A summary. New York, NJ: Macmillan.
Chung, C. J., Barnett, G. A., Kim, K., & Lackaff, D. (2013). An analysis on communication theory and discipline. Scientometrics, 95, 985-1002. doi:10.1007/sl 1192-012-0869-4
Clarivate Analytics. (2017). Essential science indicators: Selection and ranking of highly cited papers. Retrieved from https://support.clarivate.eom/WebOfScience/s/article/Essential-Science -Indicators-Selection-and-ranking-of-highly-cited-papers?language=en_US
Clark, R. (2012). Learning from media: Arguments, analysis, and evidence. Charlotte, NC: Information Age Publishing.
Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47, 1-15. doi: 10.1086/268763
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43, 51-58. doi:10.1111/j.l460-2466.1993.tb01304.x
Entman, R. M. (2007). Framing bias: Media in the distribution of power. Journal of Communication, 57, 163-173. doi:10.1111/j.l460-2466.2006.00336.x
Ewoldsen, D. R. (2017). Introduction to the forum on the retirement of concepts. Annals of the International Communication Association, 41, 83-85. doi:10.1080/23808985.2017.1289069
Falk, E., & Scholz, C. (2018). Persuasion, influence, and value: Perspectives from communication and social neuroscience. Annual Review of Psychology, 69, 329-356. doi:10.1146/annurev-psych-122216-011821
Früh, W., 8c Schönbach, K. (1982). Der dynamisch-transaktionale Ansatz: Ein neues Paradigma der Medienwirkungen [The dynamic-transactional approach: A new paradigm of media effects]. Publizistik, 27(1/2), 74-88.
Gardner, H. (1985). The mind's new science. New York, NJ: Basic Books.
Gerbner, G. (1969). Toward "Cultural Indicators": The analysis of mass mediated public message systems. AVCommunication Review, 17, 137-148. doi:10.1007/BF02769102
Hart, W., Albarracin, D., Eagly, A. H, Brechan, L, Lindberg, M. J., 8c Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135, 555-588. doi: 10.1037/aOO 15701
Jennett, C, Cox, A. L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., 8c Walton, A. (2008). Measuring and defining the experience of immersion in games. International Journal of Human-Computer Studies, 66, 641-661. doi:10.1016/j.ijhcs.2008.04.004
32 • Patti M. Valkenburg and Mary Beth Oliver
Kalyanaraman, S., & Sundar, S. S. (2006). The psychological appeal of personalized content in
web portals: Does customization affect attitudes and behavior? Journal of Communication,
56, 110-132. doi: 10.1 lll/j.l460-2466.2006.00006.x Kamhawi, R., & Weaver, D. (2003). Mass communication research trends from 1980 to 1999.
Journalism & Mass Communication Quarterly, 80, 7-27. doi:10.1177/107769900308000102 Katz, E., Blumler, J. G., 8c Gurevitch, M. (1973). Uses and gratifications research. Public Opinion
Quarterly, 37, 509-523.
Katz, E., 8c Fialkoff, Y. (2017). Six concepts in search of retirement. Annals of the International Communication Association, 41, 86-91. doi:10.1080/23808985.2017.1291280
Klapper, J. T. (1960). The effects of mass communication. Glencoe, IL: Free Press.
Knobloch-Westerwick, S. (2014). The selective exposure self- and affect-management (SESAM) model: Applications in the realms of race, politics, and health. Communication Research, 42, 959-985. doi:10.1177/0093650214539173
Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50, 46-70. doi:10.1111/j.l460-2466.2000.tb02833.x
Lang, A., Dhillon, K., & Dong, Q. (1995). The effects of emotional arousal and valence on television viewers' cognitive capacity and memory. Journal of Broadcasting & Electronic Media, 39, 313-327.
Lasswell, H. D. (1948). The structure and function of communication in society. In L. Bryson (Ed.),
The communication of ideas (pp. 37-51). New York: Institute of Religious and Social Studies. Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1948). The people's choice: How the voter makes up
his mind in a presidential campaign. New York, NJ: Columbia University Press. Lee, E. J., 8c Tandoc, E. C. (2017). When news meets the audience: How audience feedback online
affects news production and consumption. Human Communication Research, 43, 436-449.
doLTO.llll/hcre.12123
McCombs, M. E., & Reynolds, A. (2009). How the news shapes our civic agenda. In J. Bryant, & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 1-16). New York, NY: Routledge.
McCombs, M. E., 8c Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36, 176-187. doi: 10.1086/267990
McDuff, D., Kaliouby, R. E., 8c Picard, R. W. (2012). Crowdsourcing facial responses to online videos. IEEE Transactions on Affective Computing, 3, 456-468. doi:10.1109/t-affc.2012.19
McLuhan, M. (1964). Understanding media: The extensions of man. New York: Mentor.
McQuail, D. (2010). McQuail's mass communication theory. London, UK: Sage.
Moyer-Guse, E. (2008). Toward a theory of entertainment persuasion: Explaining the persuasive effects of entertainment-education messages. Communication Theory, 18, 407-425. doi: 10.1111/ J.1468-2885.2008.00328.X
Moyer-Guse, E., 8c Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion. Human Communication Research, 36, 26-52. doi: 10.1111/j. 1468-2958.2009.01367.X
Nabi, R. L. (2009). Emotions and media effects. In R. L. Nabi, 8c M. B. Oliver (Eds.), The SAGE handbook of media processes and effects (pp. 205-222). Thousand Oaks, CA: Sage.
Nabi, R. L., & Green, M. C. (2014). The role of a narrative's emotional flow in promoting persuasive outcomes. Media Psychology, 18, 137-162. doi:10.1080/15213269.2014.912585
Neubaum, G., & Krämer, N. C. (2017). Opinion climates in social media: Blending mass and interpersonal communication. Human Communication Research, 43, 464-476. doi: 10.1111/ hcre.12118
Neuman, W. R. (2018). The paradox of the paradigm: An important gap in media effects research. Journal of Communication, 68, 369-379. doi:10.1093/joc/jqx022
Media Effects Theories • 33
Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24, 43-51. doi: 10.1111/j. 1460-2466.1974.tb00367.x
O'Sullivan, P. B. (2005, May). Masspersonal communication: Rethinking the mass interpersonal divide. Paper presented at the annual meeting of the International Communication Association, New York, NY.
Oliver, M. B., & Bartsch, A. (2010). Appreciation as audience response: Exploring entertainment gratifications beyond hedonism. Human Communication Research, 36, 53-81. doi: 10.1111/ J.1468-2958.2009.01368.X
Oliver, M. B., & Raney, A. A. (2011). Entertainment as pleasurable and meaningful: Identifying hedonic and eudaimonic motivations for entertainment consumption. Journal of Communication, 61, 984-1004. doi:10.1111/j.l460-2466.20T 1.01585.x
Pariser, E. (2011). The filter bubble: What the internet is hiding from you. London, UK: Penguin.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp. 123-205). New York, NJ: Academic Press.
Potter, W. J. (2011). Conceptualizing mass media effect. Journal of Communication, 61, 896-915. doi: 10.1111/j. 1460-2466.2011.01586.x
Potter, W. J. (2012). Media effects. Thousand Oaks, CA: Sage.
Potter, W. J. (2014). A critical analysis of cultivation theory. Journal of Communication, 64,
1015-1036. doi: 10.1111/jcom. 12128 Potter, W. J., 8c Riddle, K. (2007). A content analysis of the media effects literature. Journalism &
Mass Communication Quarterly, 84, 90-104. doi: 10.1177/107769900708400107 Rains, S. A. (2013). The nature of psychological reactance revisited: A meta-analytic review.
Human Communication Research, 39, 47-73. doi:10.1111/j.l468-2958.2012.01443.x Rains, S. A., Levine, T. R., & Weber, R. (2018). Sixty years of quantitative communication research
summarized: Lessons from 149 meta-analyses. Annals of the International Communication
Association, 42, 1-20. doi: 10.1080/23808985.2018.1446350 Raney, A. A., & Ji, Q. (2017). Entertaining each other?: Modeling the socially shared television viewing experience. Human Communication Research, 43, 424-435. doi: 10.1111/
here. 12121
Rice, R. E. (2017). Intermediality and the diffusion of innovations. Human Communication
Research, 43, 531-544. doi:10.1111/hcre.l2119 Rogers, E. M. (1962). Diffusion of innovations. New York, NJ: Free Press.
Rosengren, K. E. (1974). Uses and gratifications: A paradigm outlined. In J. G. Blunder, 8c E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 269-286). Beverly Hills, CA: Sage.
Russell, C. A., Russell, D., Morales, A., 8c Lehu, J.-M. (2017). Hedonic contamination of entertainment: How exposure to advertising in movies and television taints subsequent entertainment experiences. Journal of Advertising Research, 57, 38-52. doi:10.2501/jar-2017-012
Scheufele, D. A. (1999). Framing as a theory of media effects. Journal of Communication, 49, 103-122. doi: 10.1111/j. 1460-2466.1999.tb02784.x
Shade, D. D., Kornfield, S., 8c Oliver, M. B. (2015). The uses and gratifications of media migration: Investigating the activities, motivations, and predictors of migration behaviors originating in entertainment television. Journal of Broadcasting & Electronic Media, 59, 318-341. doi:10.1080/ 08838151.2015.1029121
Shah, D. V., Cho, J., Nah, S., Gotlieb, M. R., Hwang, H., Lee, N.-J., ... McLeod, D. M. (2007). Campaign ads, online messaging, and participation: Extending the communication mediation model. Journal of Communication, 57, 676-703. doi:10.1111/j.l460-2466.2007.00363.x
34 • Patti M. Valkenburg and Mary Beth Oliver
Shah, D. V., McLeod, D. M., Rojas, H., Cho, J., Wagner, M. W., & Friedland, L. A. (2017). Revising the communication mediation model for a new political communication ecology. Human Communication Research, 43, 491-504. doi: 10.1111/hcre. 12115
Shannon, C, & Weaver, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press.
Shrum, L. J. (1995). Assessing the social influence of television: A social cognition perspective on cultivation effects. Communication Research, 22, 402-429. doi:10.1177/009365095022004002
Slater, M. D. (2007). Reinforcing spirals: The mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory, 17, 281 -303. doi: 10.1111/j. 1468-2885.2007.00296.X
Slater, M. D., Henry, K. L., Swaim, R. C, & Anderson, L. L. (2003). Violent media content and aggressiveness in adolescents: A downward spiral model. Communication Research, 30, 713-736. doi:10.1177/0093650203258281
Slater, M. D., & Rouner, D. (2002). Entertainment-education and elaboration likelihood: Understanding the processing of narrative persuasion. Communication Theory, 12, 173-191. doi:10.1093/ct/12.2.173
Stafford, T. F., Stafford, M. R., 8c Schkade, L. L. (2004). Determining uses and gratifications for the
internet. Decision Sciences, 35, 259-288. doi: 10.111 l/j.00117315.2004.02524.x Sundar, S. S., 8c Limperos, A. M. (2013). Uses and grats 2.0: New gratifications for new media.
Journal of Broadcasting & Electronic Media, 57, 504-525. doi: 10.1080/08838151.2013.845827 Tamborini, R., Bowman, N. D., Eden, A., Grizzard, M., 8c Organ, A. (2010). Defining media
enjoyment as the satisfaction of intrinsic needs. Journal of Communication, 60, 758-777.
doi:10.1111/j.l460-2466.2010.01513.x Tewksbury, D., 8c Scheufeie, D. A. (2009). News framing: Theory and research. In J. Bryant, &
M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 17-33).
New York, NY: Routledge. Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth
in knowledge. Public Opinion Quarterly, 34, 159-170. doi: 10.1086/267786 Tong, S. T., Van Der Heide, B., Langwell, L., 8c Walther, J. B. (2008). Too much of a good thing?
The relationship between number of friends and interpersonal impressions on Facebook. Journal of Computer-Mediated Communication, 13, 531-549. doi: 10.1111/j. 1083-6101.2008.00409.x Valkenburg, P. M. (2017). Understanding self-effects in social media. Human Communication
Research, 44, 477-490. doi:10.1111/hcre.l2113 Valkenburg, P. M., 8c Peter, J. (2013). The differential susceptibility to media effects model. Journal
of Communication, 63, 221-243. doi:10.1111/jcom.12024 Valkenburg, P. M., Peter, J., 8c Walther, J. B. (2016). Media effects: Theory and research. Annual
Review of Psychology, 67, 315-338. doi:10.1146/annurev-psych-122414-033608 Vidmar, N., & Rokeach, M. (1974). Archie Bunker's bigotry: A study in selective perception and
exposure. Journal of Communication, 24, 36-47. doi:10.1111/j.l460-2466.1974.tb00353.x Voorveld, H. A. M., & van der Goot, M. (2013). Age differences in media multitasking:
A diary study. Journal of Broadcasting & Electronic Media, 57, 392-408. doi: 10.1080/
08838151.2013.816709
Walter, N., Cody, M. J., 8c Ball-Rokeach, S. J. (2018). The ebb and flow of communication research: Seven decades of publication trends and research priorities. Journal of Communication, 68, 424-440. doi:10.1093/joc/jqx015
Walther, J. B., Tong, S. T., DeAndrea, D. C, Carr, C, 8c Van Der Heide, B. (2011). A juxtaposition of social influences: Web 2.0 and the interaction of mass, interpersonal, and peer sources online. In Z. Birchmeier, B. Dietz-Uhler, 8c G. Stasser (Eds.), Strategic uses of
Media Effects Theories • 35
social technology: An interactive perspective of social psychology (pp. 172-194). Cambridge, UK:
Cambridge University Press. Walther, J. B., & Valkenburg, P. M. (2017). Merging mass and interpersonal communication via
interactive communication technology: A symposium. Human Communication Research, 43,
415-423. doLTO.llll/hcre.12120 Walther, J. B., Van Der Heide, B., Hamel, L. M., & Shulman, H. C. (2009). Self-generated versus
other-generated statements and impressions in computer-mediated communication: A test of
warranting theory using Facebook. Communication Research, 36, 229-253. doi:10.1177/
0093650208330251
Walther, J. B., Van der Heide, B., Kim, S.-Y., Westerman, D., 8c Tong, S. T. (2008). The role of friends' appearance and behavior on evaluations of individuals on Facebook: Are we known by the company we keep? Human Communication Research, 34, 28-49. doi: 10.1111/j. 1468-2958.2007.00312.x
Wang, Z., 8c Tchernev, J. M. (2012). The "myth" of media multitasking: Reciprocal dynamics of
media multitasking, personal needs, and gratifications. Journal of Communication, 62, 493-513.
doi:10.11U/j.l460-2466.2012.01641.x Weber, R., Eden, A., Huskey, R., Mangus, J. M., 8c Falk, E. (2015). Bridging media psychology and
cognitive neuroscience. Journal of Media Psychology, 27, 146-156. doi:10.1027/1864-1105/
a000163
Yee, N., 8c Bailenson, J. (2007). The Proteus Effect: The effect of transformed self-representation on behavior. Human Communication Research, 33, 271-290. doi:10.1111/j.l468-2958.2007.00299.x