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ANALISIS DE LA FACTIBILIDAD ECONOMICA DE LA PROPUESTA
Although most academics expect that new measures will enrich our understanding of patterns of news and media use as well as its wider implications in the future, these measures are not yet central to academic audience research. There are individual projects that have produced interesting findings, and methodological innovation is underway both in audience research and beyond, just as it is clear that audience research can potentially benefit from more collaboration with scholars from other disciplines like information science who can bring sophisticated tools for automated text analysis, social network analysis, network analysis and the like to the table. Several of the academics we talked to also highlighted the potential for further academic-industry collaboration: “News organizations have tons of data just lying around, and we academics really need to engage more with them” says one interviewee.
But so far, several factors including (1) reservations concerning whether new methods can actually help academic researchers address the questions they consider most important, (2) concerns over the quality of the data available, and (3) barriers to effective interdisciplinary collaboration, means progress to date remain limited.
First, academic research on audience behaviour and media use has in the last decade been increasingly oriented towards understanding questions concerning cross-media use (Schrøder, 2011, Patriarche et al, 2014).
“There is no question digital is important and will become more so. But I think people right now are more concerned with understanding users’ cross-media repertoires, and so far, most digital methods seems confined to one platform only, and can’t really capture, say, the combination of watching television and tweeting about it. I think that is part of the reason people still rely a lot on surveys and interviews. They may be old-fashioned, but they are, if you will, platform neutral.” (Senior academic audience researcher active in EU-supported network of audience researchers.)
“The ultimate goal is a multi-channel approach. But so far, the approach is channel by channel, to understand and get better at using channels and tools that already exist. … [In terms of cross- media use] we can’t work from at-hand data sets. It drives me nuts when people do analysis of Twitter just because the data is easily available, and call it “social media analysis”. It is Twitter analysis. How do you know your findings apply to Facebook or SnapChat? You don’t. Same thing
with apps. We have tons of analysis of publicly available data, but that’s the parable of the drunk man and the lamp post. Is that really what you want to know?” (Journalism researcher with industry background working on impact of news)
Second, concerns over data quality loom large. As one media researcher with a strong background in technology and digital methodology writes, analysis of web metrics “remain scattered and ad hoc with few best practices to guide them” and many authors working in this field “have largely declined to explain the validity of their interpretations” (Freelon 2014). Analysis of social networks tend to focus on open networks where data is easily accessible (like Twitter) with no parallel or intergrated analysis of “dark social” networks including sharing via email, resulting in potentially quite misleading overall impressions of the nature of sharing on the web. Malcolm Parks, the editor of the Journal of Communication writes that the problem with many submissions to this leading publication is that “researchers [select] the large- scale indicators they could and were then left in the position of trying to attribute broader conceptual meaning or importance to operational indicators of convenience rather than of choice. Even more difficult problems arise when a given operational indicator appears to be valid, but is too limited to capture the full richness of the concept it presumably measures” (Parks 2014). Even with bigger data sets, academics still want questions concerning sampling, representativeness, case selection, and reliability and validity to be addressed. No data set is a complete dataset. Our interviewees generally express many of the same reservations and in addition highlight what they perceive to be the shortcomings of the most widely used commercial tools.
“A lot of [news organizations] work with Google Analytics, but frankly I think we can do better when it comes to measuring time, clicks, etc. The methods used by these proprietary services differ but are generally all opaque and not very precise.” (Social scientist researching patterns of news use)
“Most of the industry is relying essentially on advertising metrics, generally exposure. These are at best misleading if your purpose is to measure the impact of your news content.”(Journalism researcher with industry background working on impact of news)
Third, most of the methods and techniques necessary to analyze web metrics are primarily developed in information science and computer science, and less in audience research and media use research. Given the incentives of academics increasingly working in a publish-or-perish environment, established audience researchers trained in conventional social science methods may not be in a position to acquire these skills, or indeed to teach them to their own graduate students (Bail, 2014). Many projects working with new sources of data have therefore been interdisciplinary collaborations between social scientists and information scientists of various persuasions. Such collaborations are promising, and some are under way in the UK in projects that focus on news and current affairs (the Election Debate Visualization project headed by Stephen Coleman) or other forms of digital media content (work at the Knowledge Media Institute at the Open University, the Centre for Interdisciplinary Methodologies at Warwick, and the Horizon Digital Economy Research Group in Nottingham) that will in all likelihood demonstrate the utility of interdisciplinary collaborations and multi-method approaches. But such collaborations can be hard to bring about as academics disagree over theoretical and methodological issues and have professional reasons to orient themselves towards very different intellectual issues, professional incentives, and publication venues. These include access to funding within their fields and the necessity for publication in specific publications and to pursue other impact measures used in academic evaluations such as the UK Research Excellence Framework (REF).
“My sense is, to put it a bit crudely, that the information scientists have methods, and the social scientists have questions. What we need now is for people to work together, which can be hard enough, and then we need better data so we can use those methods to pursue those questions in a way that is more intellectually robust.” (Computational sociologist who has completed several interdisciplinary collaborations with information scientists on digital media use.)
“I wasn’t trained in these methods, and frankly have only a superficial understanding of them. I appreciate that they may help us in the future, but if I were to use them on one of my own projects I would need to find someone to work with.” (Senior academic audience researcher active in EU-supported network of audience researchers.)
These limitations have arisen partly because of the diffusion of the research across disciplines and the lack of coordinated inquiry. Much of the research takes place with information technology perspectives rather than social science perspectives and both have had difficulties developing techniques to adequately measure digital supply and consumption in ways that answer fundamental social questions. Similarly, the rapid expansion of data has produced few analytic methods and software to manage and investigate fundamental questions. There is a need to bridge gaps between information technology and artificial intelligence researchers and those in sociology, political science, communications and media studies to ensure tools with necessary functionality and usability are developed.
A further limitation consists in the fact that audience metrics can only ever tell part of the story, because news audiences can get a lot of information from the front page of a news website simply through reading the titles and bylines of articles but without clicking on anything. This means that news media might inform people about a lot of what is going on, but their readership statistics will only reflect a small part of this knowledge acquisition, and studies based solely on such statistics could offer a biased view of what is actually being read. For this reason, measures which offer an idea of how attention is distributed on pages, or what people look at before clicking, would be a useful supplement to currently available tools.
5.3.1
Challenges of analytical tools
The lack of readily available tools providing insight into broader issues of content availability and consumption is slowing the ability of researchers to answer deeper questions about the impact of content on individuals and society. Although a variety of basic tools for measuring supply and consumption exist, they often do not provide functions needed for social research or are not available at a reasonable price for non-commercial use. This challenge is compounded because academic researchers have less access to the industry metrics than commercial firms and often are forced to make do with rough proxies, such as the most read lists that are found on most news websites. This occurs because many researchers do not have funding that allows them to purchase statistics from commercial research firms such as comScore.
Limited development budgets and a need to limit their functions may also mean that some of the software packages used for academic research may potentially lack the ease of use associated with certain commercial packages – in particular if the potential user base for the academic-focussed tool is relatively small, it is unlikely to gain great attention and development by a commercial supplier. In some cases researchers may use general purpose data analysis and statistics tools, including programming environments such as Python and R.
Despite these challenges, there are several examples of tools developed by researchers focussing on the availability and supply of online news sources, Such as MediaCloud discussed above. Other tools being developed by teams working in this area include Open Gender Tracker, which enables researchers (as well as content producers) to analyse gender diversity in news output.
Most of the measurement techniques and tools in place which focus on content consumption were created to serve the needs of advertisers to understand what content people are interested in, where they are encountering that content in the digital work, and to coordinate advertising messages with those interests and locations to improve their effectiveness (Napoli, 2010). A growing body of techniques and tools are designed to help content providers understand the individual choices of their users and to better provide news and information that meets their patterns of use. These measures can be useful to researchers considering broader social issues of digital communication, but they provide imperfect understanding of user choices and use of content and patterns of aggregate consumption, sharing, and contribution beyond the original purposes of the measurements, just as they provide limited insights into
the structural properties of media environments in terms of diversity, plurality, and market dominance. Given the reality of cross-media use and the academic interest in cross-media use, the absence of data that is directly comparable across different media like print, broadcast, and various forms of digital is particularly challenging.
Two large-scale projects involved in trying to establish the drivers and impact of news use are the Engaging News Project at the Annett Strauss Institute for Civic Life at the University of Texas-Austin and the Media Impact Project at the Norman Lear Center at the Annenberg School of Communication at the University of Southern California. Both institutions are working with both for-profit and non-profit partners to develop their own techniques and tools to improve measurement and combine more precise data, with
existing social science research methods, to identify drivers of readers’ engagement and the impact of
news use.
These teams work to develop more precise metrics for news organizations and to introduce methods developed by experimental social scientists and by e-commerce to allow for more systematic testing of various kinds of content and presentation. Generally, researchers at both centres praise the industry and non-profit partners they work with, but also caution against exaggerating the extent to which the industry at large has embraced analytics.
“When you [compare to best practices in e-commerce] you can see how far behind the news industry really is.” (Journalism researcher with industry background working on impact of news)
“Everyone says they want to use [analytics] and all the rest. But when you get inside it becomes clear a lot of people do it poorly, that these tools meet a lot of resistance, especially when results do not confirm expectations or conventions.” (Journalism researcher with industry background working on impact of news)
Thus, academics working with industry partners suggest that while industry leaders may have developed sophisticated forms of analytics and are aware of the major limitations of the data they work with, much of the industry relies on off-the-shelf approaches for commercial decision-making and these approaches cannot be simply integrated into more rigorous academic studies asking deeper questions about digital consumption behaviour. Instead, academic researchers believe they must be used in conjunction with other established academic research methods.