Remediation,convergence,and big data

Abstract

The era of multiplatform media and big data provide new opportunities to reconsider data access by media companies. Outlined here is the discussion surrounding data access from media institutional logic and user-centric perspectives in the contexts of digitalization and big data. The discussion includes technological affordances that can be geared toward users or that merely reinforce media companies’ prominence. However, limitations of information architecture lie in its structure and the inability to facilitate navigation by users across multiple content streams. Media companies concentrate access around their own cross-platform content. Despite technological feasibility, media companies continue to choose cross-platform architecture that is structurally limiting to users. Cross-platform conceptual limits are discussed within the context of the broader socioeconomic landscape of mass media digitalization and big data.

Keywords

Audience autonomy big data convergence data architecture digitalization media logic remediation social media social responsibility

Introduction

Cross-platform media dominate the current media landscape. A recent account of Italian radio stations shows that 83% of them use some combination of the three major social media sites: Facebook, Twitter, and Google+ (Zelenkauskaite and Simões, 2015). Cross-platform viewership of TV includes 79% of viewers interacting through a second screen as they watch – be it mobile phone, laptop, or tablet (Koenig, 2014). With more platforms available, what are the limiting aspects of cross-platform information access?

Despite the evident dominance of cross-platform media, the goal of this article is to address the limitations of cross-platform media for their users by focusing on early conceptualizations of media convergence and big data. The tension presented here regards the failed promise of media companies to fulfill what Sandoval (2014) calls social responsibility. Social responsibility is safeguarded when emerging media platforms ‘have in place a process to integrate social, environmental, ethical, human rights, and consumer concerns into their business models’ (p. 32). As a result, the conceptual vision of cross-platform information architecture should embrace values that empower audiences, users, and citizens to utilize big data repositories (Lahey, 2014).

Data architecture and access have a direct influence on the power relationship between users and media companies. While media companies may design media repositories to continuously gather data about users, access to data can determine the levels of restrictions imposed by a platform or a combination of platforms (Bjur et al., 2014). Thus, data architecture involves choices that have economic and social implications (McCreadie and Rice, 1999).

In this article, the implications of information architecture as organizational choices have been positioned within the framework of remediation and big data. The idea of remediated content in its various forms has been celebrated through the promise of convergence to reduce the gap between producers and consumers (Jenkins, 2006). The rise of social media as yet another facilitator of remediation, convergence, and interactivity has presented an opportunity to reconceptualize information architecture for each of the emerging social media platforms. In turn, remediated cross-platform big data repositories provide new communicative and content-related contexts to emerge, that is, through user participation across platforms and media forms. As a result, user participation and interaction with content across platforms theoretically provide grounds to understand big data in terms of user-centric benefits. From the user-centric perspective, big data repositories have the potential to create infinite permutations and combinations of new contexts. In sum, big data can fulfill the promises of digitalization and the interactive potential of converged media. Yet these promises are still lingering. As Purdam (2014) has pointed out, users, who are actual producers of data, do not have access to that content, even if increasing amounts of data are produced by those users. Other limitations have been emphasized in terms of ethical considerations for data collection (boyd and Crawford, 2012; Fairfield and Shtein, 2014; Purdam, 2014), information accessibility (Skinner, 2013), monetization (Fuchs, 2015), and limited user-centric applications geared toward flexibility across sources as well as the lack of vision on the part media institutions with regard to the value for users (Zelenkauskaite and Simões, 2013).

This study addresses the shortcomings of the information architecture-based choices that media companies have made as they evolved through media convergence, remediation, and big data. If big data as a trend puts data architecture in a new light, to what degree has it been envisioned as benefiting users? The socioeconomic and commercial factors that influence big data have been elaborated in early critiques (e.g. boyd and Crawford, 2012; Fairfield and Shtein, 2014; González-Bailón, 2013; Manovich, 2011), including skepticism toward effectiveness of social media and big data for information sharing (Skinner, 2013). This study extends the critiques regarding social media content production and ownership of big data. In contrast to Bolter and Grusin’s (2000) focus on the remediation of media content and genre, the focus here is on the information architecture and its consequences for media-centric and user-centric contexts. In particular, I highlight the contested nature of media platforms as shaped not only by users but also, more importantly, by media companies. Cross-platform social media and big data are considered as sociotechnical contexts (Sawyer and Chen, 2003) that combine convolutions of technology, social practice, and cultural content (van Dijck, 2013). The argument presented here addresses media companies’ social responsibility for designing cross-platform media and providing ways for users to navigate through big data cross-platform repositories, just as the media companies can themselves.

This article firstly provides background to these issues, presenting a debate on the early conceptualizations of social media through its interactive potential for users, by especially focusing on users who may choose to navigate content in remediated and converged cross-platform media. Then, the relationship of remediated media to big data is discussed. This is followed by the promises offered by the early vision of information architecture to users during the process of digitalization. The following section introduces a critique of cross-platform media that highlights big data as limiting for the users. The article concludes with a discussion of the implications regarding the failed promise not only of converged interactive media but also of big data.

Background

Cross-platform media and big data

Cross-platform media and big data are positioned in this article through the lens of choices that media companies make regarding the ways they transmit information. Altheide and Snow (1979) use the term media logic to refer to the process through which media present and transmit information. If media logic is treated as media companies’ choices, then social media integration and access are the responsibility of those media companies. These choices are discussed here through the processes of media convergence and more recently the emergence of big data and cross-platform social media. If technological architecture facilitates these processes, I argue that the decisions regarding the mere structure of technology have implications for the media companies as well as users.

Media convergence and remediation

Cross-platform media can be understood by tracing the history of the vision of interactive mass media. Interactive technological affordances were supposed to include benefits for audiences, users, or consumers. The empowering aspects of two-way interaction and more flexible navigation between platforms resonate with the early studies of user participation on the Internet. For example, the World Wide Web has been praised for its potential to facilitate activism (Wray, 1999). Similarly, the vision of interactive (two-way) mass media has been presumed to provide audiences with increased benefits, such as connectivity, increased flexibility and cross-platform media access, and an overall enhanced user experience (Cesar et al., 2009). Audiences’ active participation has been linked to the increased levels of control including selection, modification, remote access, and, finally, user interaction with media archives. Users are thus elevated from passive audiences to producers and consumers who cocreate content and feed into the prosumer culture (Jenkins, 2006). With the rise of social media and its prevalence in the mass media, multiple social media platforms allow for media content to be reconfigured across multiple platforms by creating new bricolaged environments (Deuze, 2006). These new technological developments, such as mobile texting, back-channel add-ons, and social media, at first exhibit new augmented experiences bringing to the user a perception of interactivity, immersion, immediacy, and novelty (Bolter and Grusin, 2000).

Driven by digitalization and sociotechnical changes, such as the emergence of social media platforms, the rise of prosumer culture (Jenkins, 2006), and user-led content (Schäfer, 2011), mass media in the past decade have been shaped by two complementary trends: media convergence and remediation of content. Media convergence embraces various media platforms and provides new contexts for them (Jenkins, 2006). Convergence is defined as ‘the mutual remediation of at least three important technologies – telephone, TV, and computer – each of which is a hybrid of technical, social, and economic practice and each of which offers its own path to immediacy’ (Bolter and Grusin, 2000: 224). Media convergence in its broadest sense includes the technological infrastructure and affordances and communicative and societal processes such as globalization, internationalization, flexible production, the rise of the knowledge economy, or network logic (van Dijck, 2013).

Remediation considers media as continuously multiplied and reproduced across various forms and formats that originated from previous technologies. In the past decade, technology-centered digitalization continues to insist on the promise that social media platforms alongside mass media are designed to increase user interactivity and two-way participation (Sundar and Marathe, 2010). However, Fetveit (2007) has argued that convergence through the process of digitalization has merely facilitated globalized remediation. Moreover, the promise of digitalization to allow media convergence has instead resulted in a mere proliferation of media formats around the globe. Examples of such proliferation include the popularity of the entertainment genres such as reality shows.

More recently, big data has provided the opportunity for remediated and converged media archives to become live media content repositories. Such media repositories can be updated in real time and their content might include a variety of sources in different formats – such as mass media content and its associated content posted by the audiences through Twitter hashtags and/or Facebook pages created for a given show, program, or a TV station.

From the monetary perspective, cross-platform media can be beneficial not only to media companies but also to users. There is some evidence that digitalization has provided free of charge access to broadcast content. Multiple mass media companies have decided to stream their content online free of charge – for example, the Italian RAI group (RaiTV, 2014). Moreover, many TV shows have developed plotlines over time and viewers engage in a shared viewing experience by exchanging information about real-time events in a given show (e.g. Deller, 2011; Harrington et al., 2012) or have designed mobile texting as a back channel for games (Reßin and Haffner, 2007).

Cross-platform remediated and redistributed content provides benefits for companies. Companies continue to benefit from social media and its network-based potential, as they did with the traditional broadcast media. Due to convergence across media genres and remediated content across platforms, users get more exposure to the content of a given media company’s product. Also media companies can have more direct access to their audiences and users across various platforms. Thus, media companies can gain new spaces for advertising and shaping interactions with their viewers/listeners (AssociationMedia, 2013).

Opportunities for social responsibility: Digitalization and big data

Technological reconfigurations over the past decades have provided at least two opportunities for the mass media and social media companies to reconsider their information architecture. The first one discussed here is digitalization, the second one is navigation through big data repositories – yet both of them have failed to fulfill the opportunity to reconsider the information architecture for the users.

Digitalization has facilitated the interplay between various forms of media, telecommunications, and information technology services, resulting in increased interoperability between various sources of information (Enli and Syvertsen, 2007; Jenkins, 2006). Interoperability has shaped mass media to increasingly include network-based and produsage-based platforms, mobile technologies, short message service, and social media such as Facebook or Twitter (Bolin, 2010; Bruns, 2008; Doyle, 2010). If interoperability facilitates adaptation and context awareness, it should have provided an increased value to user interaction (see Seffah and Javahery, 2005). Yet interoperability has not eliminated all barriers for users. Real time and digital archives of TV shows, radio programs, online newspapers, and magazines – along with their associated social media streams produced by their audience, listeners, and readers – act as a big data repository to further the commercial goals of a specific media company. They all constitute a big data repository because of the variety of data streams that are constantly generated by media professionals as well as audiences’ reactions on social media through commentary, for example, following shows through associated hashtags. All those data streams provide real-time access to the interest of the users which can further be sold to marketers. In such a big data repository, data can be more easily compared, analyzed, contextualized (Menezes and Carvalho, 2009), and monetized.

While debates regarding user interaction and company benefits continue to be relevant in a consideration of social media, the notion of data access becomes relevant in a new light due to the emergent notion of big data. Big data is broadly construed as data streams from social media and mass media, which can be used as a unified data repository. A big data repository is a constantly updated real-time data archive that includes a variety of data sources in different formats – in the media context, those formats are audio, video, or text. Those sources can include various online platforms that range from social networking sites such as Instagram, Twitter, Facebook, and video-based channels such as YouTube. Big data can also include any other traditional media’s digitized data sets in any format – text, audio, or video – such as newspapers, journals, books, TV, or radio. Data streams consist of a combination of user-generated content (UGC), professionally generated content (PGC), and machine-generated content (MGC).¹ With the rise of cross-platform content integration generating increasing amounts of two-way interactive media, including social media, the sum of their generated data constitutes what are termed big data repositories, which are often owned by media companies.

Big data has emerged as a volume-driven paradigm that allows for ‘integration and sustainability of very large datasets’ (Bourne, 2014: 194). The concept of big data has been developed to discuss the increasing variety and amounts of data sources derived from digitalized platforms. It emerged from unprecedented growth of digital data on the World Wide Web (Gantz and Reinsel, 2012). Its exponential growth has been described by popular media as the ‘information revolution’ (Mayer-Schönberger and Cukier, 2013) and has been embraced by marketing researchers as a potential new way to understand consumer behavior (Becker, 2014).

Mayer-Schönberger and Cukier (2013) and Davenport et al. (2013) have popularized big data by pointing out some of its potential transformative uses. For example, big data has been elevated from an enabling force to create new products and services by responding to changes in usage patterns in real time to having the potential to treat and cure threatening diseases. For this study, big data are considered as a sum of data sources at the center of at least two forces – the prominence of social media in mass media and their socioeconomic contexts – both of which have provided new ways to reconsider architecture infrastructure. Through social and interpersonal media, users can interact with mass media – radio, TV, and print media. As more cross-platform content streams get interconnected, the assumption is that each platform adds complexity to data access and ownership. Each owner of the data competes for maintaining the users on their own site, portal, or TV program. Similarly, each media company, being an owner of the data, strives to maintain control of its access. On one hand, TV links not only to their own programs but also user to participation across different platforms through their web pages. On the other hand, Facebook tracks and monetizes user activities on Facebook. While the benefits of big data to companies are evident, it is not clear how cross-platform interconnectedness can benefit users.

Choices of data integration from the media companies’ perspective proposes at least two potential scenarios when new data streams (e.g. social media) emerge in addition to mass media. Those two are horizontal (egalitarian) or vertical (hierarchical) data integration. The horizontal data integration scenario foresees cross-platforms as an enhanced vehicle for content stream fluidity and enhanced integration – based on the early visions of interactive technologies (Miccio and Mele, 1997). Vertical data integration focuses on media companies as one-to-many technologies that continue to emphasize media companies and places them at the center of other platforms.

Cross-platform remediated mass media critique

As noted in the horizontal scenario, remediation of social and mass media supports benefits for users because of the unrestricted ways to access platforms and new informational contexts. Yet each big data source, including mass media and social media, poses challenges because of spatiotemporal dimensions of information access. The limits of the current information architecture are illustrated below by cross-media platforms in TV, radio, and print media and their social media counterparts.

Spatiotemporal limits

Social and mass media streams have inherent fundamental spatiotemporal differences. For example, content search, display, and access differ structurally and organizationally on Twitter, Facebook, and traditional mass media. Content can be searched in the entire repository of Twitter. Facebook allows for searching within a given stream. Radio or TV content can be searched based on the programs. Digital content on newspapers can be searched by key words. Ultimately, user interaction can be accessed depending on whether Web sites enable interactive services and store these interactions.

The temporal dimension consists of degrees of persistence or ephemerality in accessing content during a specific period of time. Some traditional mass media have been based on the specific time when audiences are expected to tune in. Yet some mass media events covered online can be accessed immediately through Facebook or Twitter archives. User interaction via Facebook or Twitter for a given program can occur before, after, and during the show. Internet distribution of the content provides an extended viewing time – ‘on-demand’ viewing, that is, time-sensitive programming, with extended viewing options not tied to a specific schedule, in contrast to the traditional TV programs that are strictly tied to the channel’s schedule. Similar time-related constraints have been observed in the convergence and remediation of traditional mass media – radio, TV, and newspapers. Marwick and boyd (2011) refer to this constraint as time collapsed where multiple contexts on the Web merge or overlap. Bolter and Grusin (2000) refer to immediacy as being the driving mechanism of the remediation of media thus providing users with a perception of increased control. Due to these temporal dimensions, true dialogical conversation between audiences and media is still illusory.

Remediated radio, TV, and newspaper limitations

In the context of digitalization, radio data include traditional radio transmission that is mostly listened to in cars and via additional platforms, for example, social media that is geared for user participation. Radio stations have historically integrated a large proportion of audience participation through talk back shows (Horton and Strauss, 1957), ranging from calling via landline phones and mobile phones, through text-based interactions via mobile texting and more recently via social media. However, the relevance of the radio content remains time sensitive. Even if radio can be accessed via digital formats on the Internet, via digital terrestrial broadcast, and smartphones (e.g. RTL102.5.it), the question is how this participation can be meaningful after the show is over.

TV formats have been influenced by the transition from analog to digital broadcasting (Colombo, 2004). Digitalization has influenced the overall format of the programs (Beyer et al., 2007; Lotz, 2007). For example, users may take part in live shows and follow the story lines through mobile texting and Twitter hashtags that are made available during each show. Sports programming also includes fan participation through fantasy sports entertainment. Political debates also include the use of Twitter (Elmer et al., 2015). Digitalized content includes audience participation that combines reality show voting, call-in, and tweets to the TV show or texting. TV content is distributed through a variety of platforms – TV, satellite TV, cable, the Web, or mobile devices. Yet how do they form coherent environments for viewers?

Within their temporal constraints of production, newspapers have adapted to digital online production and thus, for companies, have become a useful resource for big data analytics. The physical paper-based artifact has been remediated through an online digital counterpart that is accessible to audiences anytime (Wurff et al., 2003) and has more flexible production times. Stories can be updated and/or edited more frequently and distributed through multiple platforms (print and the Web). Various online news portals incorporate user content through specified rubrics such as letters to editors and more recently with user comments regarding a given news story or through the citizen journalism rubric of a given news outlet. Newspapers and magazines have their followers on social media platforms, in addition to their Web sites, which constitute an ever-changing data flux being encapsulated in text and audio-visual formats. Yet users are treated as consumers and interactivity is offered as a mere illusion. A comparative analysis of British and Swedish online newspapers has shown that users participated in a reactive way by responding to the content initiated by the media companies rather than in truly interactional ways (Jönsson and Örnebring, 2011).

Cross-platform big data critique

The emergence of big data has opened a new space for debate regarding its potentials for users. Yet cross-platform big data adds to the list of criticisms related to the aforementioned digitalization. Even if all these coexisting media platforms and genres have potential as a big data repository for large-scale data analytics and monetization by media companies, such repositories result in questionable benefits for the users. Cross-platform big data repositories create limitations for user navigation, something which is discussed in the following subsections. Limitations are discussed further in terms of fragmentation, hierarchical access, and the proprietary nature of content. These limitations point to two key implications: the lack of user agency and the limits of information architecture for audiences.

Fragmentation across and within platforms

Digitalization has facilitated new content creation. As Jenkins (2006) has pointed out, digitalization has allowed for the same content to be remediated across platforms by creating new media genres. Remediated content, however, across media platforms functions independently, isolated within each platform’s own sociotechnical system and practices (Zelenkauskaite and Simões, 2013). Each mass media platform consists of multiple content streams that are redistributed across multiple platforms. There are beneficial and limiting aspects of cross-media platform fragmentation. They are discussed in two different ways – within a single platform or across platforms.

In a traditional media setting, each platform contains multiple media content types – TV programs, radio programs, and news stories in the newspapers. Therefore, fragmentation within a media stream occurs when users make choices between various media platforms – be it TV, radio, or magazines. Yet, in the past, each media company used to specialize in its own medium. Through the process of convergence and remediation, media companies distribute content across traditional and social media platforms. As a result, fragmentation occurs within a given platform and across media platforms. Through these multiple platforms, media companies generate a large pool of audiences, which is good for business. However, fragmentation becomes a negative consequence for the user experience across these platforms. To access multiple sources of data, the user first needs to access a specific platform. Fragmentation within media occurs when media content is distributed across various media devices and streams; this disperses the data. When disconnected, each platform isolates its content by a given media brand and creates fragmentation across streams, where each platform has its own content and sociotechnical norms. However, each platform is part of big data, a repository created and accessed by media companies. For example, TV stations integrate UGC such as Twitter, Facebook, or mobile texting in their programming. Big data information streams are fragmented across platforms such as TV, Facebook, Twitter, and mobile devices. Mobile devices such as smartphones can access TV (if the service is available) and Web-based applications such as social media platforms.

Similarly, cross-platform fragmentation occurs when media companies remediate their content across platforms and multiple applications or devices. For media companies, such an approach facilitates data distribution and increases revenue. User activities across these platforms can generate further revenue since all of these viewing activities can be traced and recorded to potentially be added to big data repositories. Such data aggregates can be monetized either directly by selling data to a third party company or indirectly via targeted advertisements. Specifically, the logic of fragmentation for media companies is geared toward greater content dispersion to a larger audience. Content fragmentation might be seen as beneficial to users since more users can access content relevant to them across their preferred platforms. However, cross-platform media are used to give a false illusion about the prominence of a given platform. For example, a TED talk (tedtalk.com) generates a cumulative view count on their own platform for a given video that combines views from any other platforms where the video gets shared such as YouTube or Facebook. Such a count can provide a false sense of popularity to other viewers. For companies, such popularity can be monetarily advantageous by giving more opportunities for advertisement.

Some platforms are more inclusive than others. Digital aspects of remediation have impacted cross-platform media. On each media platform, each program’s specific practices are treated independently to increase its relevance to the viewers. Yet cross-platform media also segments content into specific spaces, owned by specific media companies. For example, Facebook content streams are on Facebook, but they are also streamed on TV. It allows for media companies to maximize exposure to the content. However, it can be challenging for the users. If a user wants to watch a TV show and view associated tweets, one has to use Twitter to access those tweets and TV or a Web site to follow the content of the show. And if they want to interact with viewers of other shows they have to join groups with cumbersome admission procedures to navigate across platforms or media content. This model contrasts sharply with the traditional mass media model where a given user can simply switch from one channel to another. However, multiplatform social media and mass media navigation are not fluid across content and platforms.

Hierarchy and access

The fragmented nature of the data can be circumvented by converging media into unified streams. Each mass media company’s data are stored in its own repository and it is owned by a specific media company. For example, the Web sites of traditional media such as TV or radio include information about the station or broadcaster, its user interaction, and video or audio of shows. Even if all data are available to users, they are not structured in a linear and accessible way. Some content types are more prominently displayed than the others and thus influence big data construction and collection. If a media company, a TV station or a radio station, includes Facebook interactions into their programs, they can link user interactions to their web pages to increase their own visibility. However, if users want to search information about a given program on a particular TV station, they have to do so by accessing their content through that specific TV station.

Data in mass media – even across multiple platforms and formats – are based on hierarchical information access and display. Access through the search function in multiplatform remediated content becomes problematic. For example, TV enables programs to be viewed through a specific channel and then each program may contain other media sources such as social media. The Web may host TV on its platform. Within a web page of a TV station there are various sources of data. These data sources may include TV video or audio streaming, information about a given program, and a community section that delivers information from external sources such as social media platforms.

Media companies remediate their content on various online platforms. Yet, on each platform, users access media companies’ content through their ‘gateway’. Such hierarchical information access includes the companies’ content across platforms. If we assume that each platform is also based on its own sociotechnical system, then each platform functions in its own particular way and has its own audience. For example, Facebook users cannot freely connect to Twitter users. Their navigation is embedded within the given platform. Some attempts to reconcile this issue have been made through an automatic option of reposting across platforms. However, search as a form of navigation across platforms is not available. Therefore, data hierarchies do not allow for horizontal information access even if it is supposed to enable users’ fluid navigation across media companies and platforms. If that were the case, users would be able to benefit by navigating across media content, regardless of the data ownership (if they chose to do so), especially since there is evidence that users actually engage simultaneously with multiple media companies across multiple social media platforms (Zelenkauskaite and Simões, 2015).

From the information organization point of view of a given company, hierarchical information access architecture has multiple benefits. Hierarchical architecture provides structural order which is based on a mutually exclusive categorization of items. Information categorization has implications for searching, that is, by facilitating the search within a given platform. If someone knows which specific categories are sought, they can be easily found through the hierarchical tree of information since hierarchical information is conserved as an emblematic starting point in information organization (Morville and Rosenfeld, 1998). If cross-platform data are designed through hierarchical access, such an approach inevitably inherits the limitations of traditional mass media, where a specific media platform becomes central. In the case of social media and mass media, centrality is defined by the entry point. The entry point is a TV station, a Web site, or a social media platform – each of them serving as a central point that determines the users’ navigation. If a user wants to access information across platforms, they must access it through a central source that introduces bias in their searching.

The hierarchical data access model also locks in data within the context of a given media company. Each of the data points becomes the property of a given mass media company, even if they constitute big data repositories that could be used across media companies or across platforms. For example, with multiple platforms and media companies’ content available to the users, they can interact with a particular TV program or radio station on the station’s Twitter page. Yet users can only mention and tag the TV program or radio station within their profile. However, it becomes more difficult to view all information across platforms and across media companies, unless users decide a priori to follow specific media outlets and do so through their individual social media pages and platforms.

Proprietary nature

Hierarchy and fragmentation are closely related to the proprietary nature of the data. If a user was interested in accessing all of the data from a TV or radio station, including video, audio, and social media data from user interaction, only a portion of these data would be accessible. Given that information is fragmented across various platforms, each platform can host the big data-unified repository based on different degrees of limitations. Proprietary limitations to big data repositories are driven by the limitations of a platform’s affordances. TV, for example, can integrate social media streams or mobile telephony on one screen, but the web page has to be displayed on a different screen. Social media platforms can incorporate user content streams, but not the web pages or TV content.

A given media platform also holds copyright or secures access to the content. Regardless of the potential of large volumes of data for users, not all data are readily available. Vis (2013) argues that data do not exist by the virtue of being generated on multiple streams, it is made. In other words, it is the owners who actually enjoy the benefits of the data even if it seems that fragmented data points are available everywhere. Fragmentation is further complicated by the hierarchical data architectures that are embedded in Web design, which most likely include multiple sources. Moreover, the flexibility of the hierarchical architecture is further limited by the levels of access to data sources due to their proprietary nature.

Discussion: Implications of lingering challenges

On the one hand, multiplatform mass media provide new opportunities for users to access mass media-related content across various platforms. Cross-platform media for media companies allow them to construct real-time big data repositories. Yet, on the other hand, user interaction with a given platform, and especially across social media platforms, remains limited due to competing company-centric and user-centric perspectives of cross-platform media, architecture, and the data’s proprietary nature. In spite of the potential of big data for users, cross-platform challenges remain socioeconomic rather than merely technological.

The implications regarding digitalization bifurcate into media company-centric and user-centric perspectives. This differentiation reflects van Dijck’s (2009) treatment of data consumption and production as operating within a media company, the consumer, and the advertiser. While prosumer culture for media companies provides promises to elevate the user to a position of content producer, the actual possibility of content production remains limited. Moreover, the current media’s data organization is based on a hierarchical information access that facilitates companies that own data to remain in control of data access across platforms. Similarly, big data repositories are constructed for media companies for user analytics based on mechanisms such as page views, clicks, and comments rather than for user benefits. In this way, media companies can not only analyze and track their audiences but also sell data to third parties. Social media companies such as Facebook in fact provide access to their users’ data to marketers who can tailor their advertising based on behavioral and demographic user data.

As it happens, the empowering aspects of technology are restricted by commercialism. From the media-centric perspective, the commercial benefits of digitalization are evident. Media companies’ content can be redistributed and multiplied, thus driving more revenues, to achieve greater exposure and larger audiences across multiple platforms. Thus, the technological advancements are in line with the commercial nature of a media-centric perspective deeply rooted in a revenue-driven paradigm. Media companies invest in their products to be able to maintain current audiences and gain new ways of accumulating revenue; they have shifted from direct revenues from products to services, including advertisements, to more recently selling user data to a third-party provider. With more content streams, labeled as the big data phenomenon, more information streams can be reached. Such technological advancements can be further monetized through engaging larger numbers of users. While mass audiences in traditional media have been monetized by being exposed to advertisements to create brand recognition, currently multiple streams of data in a big data context algorithmically help to further monetize this process by personalizing advertising. Some pundits point to the potential of big data for a culture of innovation and discovery, yet interest in big data is still motivated by the desire ‘to catch up with the market’ (Franks, 2012: 255).

The user-centric approach becomes problematic in light of the cross-platform big data repositories because of how data architecture is designed. Even if users engage in interacting with multiple media outlets across platforms, such interactions are not supported by the design of the platforms. Each platform is designed for users to navigate within its parameters – for example, the links between Facebook and Twitter have been established only to facilitate reposting of the same content across those two platforms. However, users cannot search for content across platforms through a unified search mechanism. Multiple sources can provide additional platforms for more fluid user navigation and interaction based on content, rather than based on media companies (Zelenkauskaite and Simões, 2013). However, at the moment, users can navigate only within a given mass media company’s or platform’s content by logging in to it or subscribing to it. To reach any of the social media streams, an audience member needs to do so through the program’s Web site to manifest the quantifiable brand loyalty.

Accessing the media content across platforms is not horizontal, but vertical. Vertical information access is designed by a specific media company, despite empirical evidence indicating that users are interested in navigating across platforms even if the navigation across media platforms has not been facilitated technologically (Zelenkauskaite and Simões, 2013, 2014, 2015). Vertical information access has been chosen, notwithstanding the proposed attempts to extract the value of social media to the users as a way to redistribute mainstream mass media content and reconnect with audiences (Newman, 2011). Yet Roscoe (2004) has attributed convergence, divergence, and dispersal to contradictory forces in TV production, delivery, and viewing.

The proprietary nature of the data is not the only obstacle to cross-platform social media. Multiple media companies possess access to user navigation data, but users do not. Therefore, users might look for the cross-platform navigation that enables them to access the media companies information flows currently denied to them. But media companies do not have the incentive to create such systems because the resulting partial data cannot be effectively used for data analytics and sold to advertisers. Further, the mere resurgence of a big data paradigm does not mean it provides benefits to users. On the contrary, big data was crafted based on choices and decisions that companies made for data gathering and analytics rather than with users’ benefits in mind.

Conclusion

This study has addressed the limitations of information access that are foregrounded in the sociotechnical structure of cross-platform media. The limitations include the failed promise of interactive media by highlighting incompatible structures of cross-platform media sources through spatiotemporal dimensions. Limitations of big bata cross-platform media include hierarchical access and the data’s proprietary nature.

This study has several implications. First, choices regarding technological architecture are ultimately based on a vision rather than the mere feasibility of implementation. When it is not a question of mere technological feasibility, the alternative view proposed in this article is that the current social media hierarchical implementation favors choices that increase profits for media owners. The current implementation of cross-platform media deromanticizes the early vision of technological affordances and promises of convergence in mass media to facilitate information access to users. Access remains limited to users, notwithstanding the transformation of mass media and the availability of content across platforms.

Implications regarding data ownership are that big data is monetized via individual companies by accessing and potentially extracting user behaviors across platforms. Yet big data is neither designed nor available to users. Users are given only one part of the story – the one that they are allowed to see.

Implications regarding hierarchical structure derive from the fact that social media simply represent the sum of the parts of remediated traditional technologies, some of which are more egalitarian than others. While users have been provided with some increased levels of interactivity and navigation, the current paradigm of big data and technological solutions do not provide evidence to serve users. Cross-platform remediated combinations continue to maintain the hierarchical structure of a media company. The same holds true for the hierarchical structures from which social media are constructed. New technologies are geared toward media companies rather than a user-centric design. The conceptualization of information architecture and technological affordances are positioned here as fundamental deterministic structures in which technological components of interlinked mass media and digital media situate users.

Broader theoretical implications of analyzing cross-platform limitations produce a wider spectrum of questions about social media that goes beyond single platform limitations ‘approaching them as dynamic structures that evolve in close connection to each other and to culture at large’ (van Dijck, 2013: 150). The current architecture that media companies adopt continues to accommodate the big data benefits for media companies, while leaving the increased data sources less visible for users and audiences. Manovich (2013) considers the invisibility of data as a product of intricacies of the software, the same software that interconnects cross-platform media. The lingering limitations of data access and the data’s proprietary nature are presumably not going to be easy to overcome since companies might not want to give up their control over data stream management. A user-centric perspective conceptually compromises existing business models that consider users as a monetized asset. Revenue-based media logic might see a threat within unified user navigation across platforms since it facilitates potential exposure to the competing media companies.

To recapitulate, cross-platform navigation remains an idealistic vision rather than a reality; the variety of data sources are subject to proprietary-based resistance to share data and make them available for user navigation across platforms, regardless of their potential. Even if media companies have included multiple platforms to provide their audiences with more agency through technological affordances (Sundar and Marathe, 2010), multiplatform integration has limited interactivity and limited agency. While user agency has been seen to be empowered through new technologies (Jenkins, 2006) where ordinary people can be part of the production process, others caution that agency remains limited (Van Dijck, 2009). For the media company, data gathered across platforms are at the frontier of big data analytics. This affords an immediate access to a variety of data points available through multiple platforms and a combination of data sources that range from traditional forms of communication, for instance, via TV and social media platforms or mobile phones.

Finally, a lack of corporate social responsibility (Busch and Shepherd, 2014; Sandoval, 2014) or monetization of the digitalization process remains unsolved. Cross-platform media maintain dominance in mass media contexts; they are still monetized. Data architecture choices should be regarded as a debate across multiple stakeholders, including involved citizens, to foster civil society or the ability to access any produced data across platforms, or the possibility to manage those data based on individual needs or preferences across any of the used platforms. Perhaps such a vision requires the leadership from policy makers and directives that should provide priorities to include citizen benefits as a core requirement of innovation beyond just mere reconfiguration of technology. In light of the tensions between media convergence, remediation, and fragmentation, the access to myriad data sources and increased user interaction via social media provides a rationale to rethink mass media in a big data context. Big data leaves us with unanswered questions regarding its consequences for the current data architecture and so far limited cross-platform and cross-media navigation to users.

Footnotes

Acknowledgements

I would like to thank Dr. Robert Mason and my colleagues Dr. Ernest Hakanen and Dr. Ron Bishop and for their close readings of the various drafts of this research. I would also like to thank the two reviewers for their constructive comments as well as the editors for their tireless work.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Note

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