The work I have done as as guest professor at Södertörn university during autumn 2015, within a project run by my colleague Lars Degerstedt at the School of Natural Science, Technology and Environmental Studies, has come to an end, but is now slowly developing into a jointly written article. Below is the framework around the concept of “media intelligence” that we are trying to develop. The article ought to be finished within a week or so.
As the arguably leading business intelligence company in Scandinavia, Cision—with a history dating back to 1892 under the name, Svenska telegrambyrån, a company that provided press clipping services in Sweden—boasts online of being a global enterprise within communication and media intelligence. “Cision serves the complete workflow of today’s communications, social media and content marketing professionals” (Cision, 2016). Yet, what does the specific notion of media intelligence actually mean? Basically, it refers to various forms of updated media monitoring practices—both manual and automatic—foremost regarding print and broadcast media. Obviously, online media has also played an increasingly important role for the media intelligence business during the last two decades. Media intelligence can also be understood by comparing it to adjacent intelligence fields. There are for example analogies to the notion of intelligence operations, as these are executed within the military domain. Military intelligence is, in short, a defense discipline that exploits a number of information collection practices and strategies in order to provide guidance for commanders. Beside military usages, computational intelligence also offers some insights. In automatised versions, crawling the social web to identify relevant conversations for example, media intelligence resembles artificial intelligence, thus linking the notion of intelligence to both machines and systems as well as networks. Rather than sticking to a strict scholarly media and communication research perspective, media intelligence should hence be understood within a larger framework of military and artificial intelligence, as well as more traditional forms of business and market analytics, adjacent to fields as strategic communication, public relations and communication management.
If military intelligence is sometimes divided into strategic, operational and tactical intelligence, the same basically goes for media intelligence. Within the commercial business sector media intelligence is often roughly divided into: (A.) business intelligence (on a particular company level), and (B.) competitive intelligence (between similar companies regarding for example shared markets). In general, the latter is different from the former since it uses and analyses data outside company firewalls. However, during the last decade—mainly due to profound technological changes brought about by digitisation—the specificities of and boundaries between business intelligence and competitive intelligence have been modified. When society is gradually turning into a market of different mediated “value networks”, as Sven Hamrefors argued in 2010, “communication functions can no longer stay in their restricted domains and only deal with traditional communication issues” (Hamrefors, 2010). On the one hand, intelligence on a strategic business-to-business-level hence should not be separated from a more practical business-to-consumer-level. On the other hand, an increasing number of companies (foremost within the tech domain) operate in different market segments, making it more or less impossible to intelligence and monitor all relevant markets. The most obvious example is Google, which started with search, soon began making operating systems, run different forms of content platforms—and now even produces cars. The same can be said of an international media group as Schibsted who does business in a number of different digital domains (publishing, online marketplaces and services).
In more concrete terms, media intelligence uses data and computer science methods to analyse both social media and editorial media content. In general, within business intelligence today, it is often argued that a brave new world of insight awaits intelligence companies and their customers, if they have the courage—or the financial abilities—to analytically start working with the exponentially growing volumes of unstructured and semi-structured data—especially from new data sources as machines, sensors, logs, and (non-textual) social media and streaming data. Basic implementation for media intelligence involves curating data, keyword references and semantic analyses, as well as natural language processing via machine learning algorithms. In essence, most practices and operations are concerned with turning text into data for analysis. Text is hence still the dominant modality for most media intelligence operations, yet other modalities (images, sound and video) have during the last decade become increasingly important, especially in different social forms.
Most forms of media intelligence, departs from the ways in which we currently are enmeshed in an “interconnected communications ecosystem wherein social and traditional media sources feed each other for stories and conversation, and those conversations are supercharged by social technology” (Nuccio, 2015). Media intelligence hence refers to computational solutions that tries to synthesise innumerable online conversations into (more or less) appropriate insights that allow companies and organisations to manage, and sometimes even measure content performance and trends—with the paramount purpose to better forecast business strategies. As is to be expected, companies working within the intelligence business sector offer quite different suggestions as to what media intelligence actually means: “Media intelligence is the process of gathering all the data available through social media and news media outlets and analyzing the data to allow for better business decision making”, according to CustomScoop (2016); Volicon is said to be the “leading provider of enterprise media intelligence solutions serving the needs of broadcasters, networks, cable operators, and governments worldwide” (Volicon, 2016); and M-Brain states that its media intelligence solutions are designed to “monitor and measure your publicity and reputation” (M-Brain, 2016). What these companies have in common, however, is that they gather massive amounts of data points from user-generated content on social media sites, blogs and comment fields, combining these with traditional mass media output and other forms of publicly open data, all in order to provide—and ultimately sell—real-time insights and suggestions based on verifiable data. Media intelligence is hence, always about selling trust.
Some scholars have argued that during recent years media intelligence has witnessed a social transition, from various forms of social media monitoring to computational driven (social) media intelligence. The latter is said to be better equipped to minister noisy sociality, with an ability to uncover valuable insights hidden in the social media chatter (Moe & Schweidel, 2014). Research related to various forms of military intelligence, have furthermore tried to identify and forecast civil unrest and radical mobilization by mining textual content in open-source social media (Agarwal & Sureka, 2015). In general, social media intelligence is based on a rudimentary data management model where social data is segmented—from automatically categorised subsets of social data, to customising rules or filtering, based on criteria like date, location, web page type, sentiment and gender. Segmentation can also be done on more specific data, for example regarding Twitter or Facebook statistics (retweets, likes, comments, media type etcetera.) In essence, data management within social media intelligence collects massive volumes of data and separates it into structured and manageable packages that can help answer particular questions via different forms of machine learning and data mining. The notions are similar; machine learning and data mining often overlap. But they also differ in that machine learning focuses on prediction—based on known properties within the collected data—whereas data mining is about the discovery of unknown properties in the same data.
Then again, if sociality has recast media intelligence during the last decade, modalities of media content is another alteration. Traditionally, business intelligence companies have relied on textual offerings—basically because machine learning algorithms need text based documents (and databases) to be able to perform automatic analyses of large scale data sets. The modality of text has, in short, been default, not the least apparent in the ways companies within the business intelligence sector have advertised themselves: “Keep track of what is written about you, your company or your competitors” (Cision, 2016); “Infomap:r is a system for predictive analytics and text mining” (Infomap:r 2016); “We keep your organization up to date on what is written and said about you and your business environment” (Newsmachine, 2016).
We argue however, that if online media has experiencing a shift towards the social, at the same time online interaction has increasingly been enriched with images, sound and videos. These new media modalities have brought forth changes that are currently having profound effects on the media intelligence business. The before mentioned infographic from Domo serves as a vivid illustration of challenges in both social and non textual media form(at)s facing the intelligence business. If YouTube has been the epitome of an ever increasing non textuality of the information landscape during the last decade (Snickars & Vonderau, 2009), the blended mix of Facebook posts in different modalities in many ways acts as its social counterpart. However, ‘social’ and ‘multimedia’ is also converging. In January 2014, for example, Facebook announced an increasing shift towards visual content, “especially with video … In just one year, the number of video posts per person has increased 75% globally and 94% in the US” (Facebook, 2015a). In fact, during 2014 Facebook had an average of more than one billion video views every day. Social video is thus an increasing trend, and the release of Facebook Instant Articles in May 2015 was consequently aimed towards the ability of watching audiovisual news material seamlessly. “Zoom in and explore high-resolution photos by tilting your phone. Watch auto-play videos come alive as you scroll through stories. Explore interactive maps, listen to audio captions” (Facebook, 2015b).
If business intelligence in automated forms have traditionally relied on text mining to monitor, detect and analyse plain online text sources, the transition to new social media modalities hence causes difficulties, both conceptually and technologically. If humans can perceive their surroundings naturally in visual form, “this undertaking is quite challenging for machines”, according to Damian Borth. Within the field of computer science machine learning in non-textual forms is, in short, utterly complicated. “The lack of correspondence between the low-level features that machines can extract from videos (i.e., the raw pixel values) and the high-level conceptual interpretation a human associates with perceived visual content is referred to as the semantic gap” (Borth, 2014). Nevertheless, even if media intelligence is struggling with new social media modalities, companies within business intelligence are also increasingly trying to cope with this semantic gap. The media intelligence company Opoint for example, are said to be “the only player in the market that monitors real-time radio and TV… [and analyses] all types of media.” Sound bites are, for example, delivered direct to customers; that is, Opoint are not using speech recognition software transforming sound into text (Opoint, 2016). Another similar company, Lissly asserts that its “tool collects, sorts and visualizes data from different digital media.” Lissly offers its customers to be able to “listen to the conversations in your market” (Lissly, 2016). In fact, the metaphor of listening is often used today within the media intelligence business, a semantic indication that other media modalities than text are becoming continuously more important: “Notified takes social media listening and management to the next level” (Notified, 2016); “We believe that the world will be a little bit better if we listen more” (NewsMachine, 2016); “There is power in listening to what customers and stakeholders are saying about your business through social media platforms” (M-Brain, 2016).
The major challenge that all business intelligence (based on other media modalities than text) are faced with today, is the somewhat paradoxical movement away from the content of communication towards the medium of communication. On the one hand, there needs to be a market demand for this kind of transition to occur—that is: the request of monitoring other (or new forms of digital) media (as data streams). At present such demand is still by and large insufficient, yet mainly because media intelligence algorithms still cannot produce appropriate results from non textual information. On the other hand, the transition—or perhaps dialectics between content and medium—also resonates in an interesting way with debates within classical media theory as to what constitutes the bias of communication. Content and medium have, in short, always been intertwined. Following Harold Innis in the 1950s, and his belief that the stability of cultures depended on the balance and proportion of each particular media form—from clay to papyrus—he claimed that each medium embodied a certain bias in terms of organisation and control of information (Innis, 1950). Marshall Mcluhan’s 1960s media theory, where the medium itself constituted the message, followed Innis ideas closely—or as McLuhan famously stated: “I am pleased to think of my own book Gutenberg Galaxy as a footnote to the observations of Innis” (McLuhan, 1964). Then again, similar ideas have also been put forward within research fields associated with media intelligence. So called “media richness theory”, for example, have been developed within organisation and management studies to describe a medium’s ability to reproduce information. Basically, communications that require a long time to enable understanding are lower in richness. “Rich media are personal and involve face-to-face contact between managers, while media of lower richness are impersonal and rely on rules, forms, procedures, or data bases”, according to Richard L. Daft and Robert H. Lengel (1986). Following media richness theory, face-to-face communication is thus perceived as the richest media form since it provides immediate feedback, and such ideas also resonates in the contemporary work of Sherry Turkle, as in her new book, Reclaiming Conversation. The Power of Talk in a Digital Age (2015). Daft and Lengel’s media richness theory was introduced in the 1980s to help organisations cope with various forms of communication challenges. In an equivalent way these ideas can be today be helpful when trying to explain the conceptual and technical obstacles facing the media intelligence business, regarding for example social competitive intelligence and media analytics.
Min kollega Alexandra Borg har idag skrivit en utmärkt understreckare i SvD – Bilderboken uppfinner seendet på nytt. Borg är litteraturvetare vid Uppsala universitet och håller så sakteliga på att etablera sig som landets kanske främsta kännare av bokmediets digitala konvulsioner (tillsammans driver vid sedan två år ett nätverk kring just bokmediets omvandling, med en kommande workshop i Malmö om några månader). Framför allt är Borgs resonemang kring hur digitaliseringen revitaliserar det analoga bokmediet intressant. Digitaliseringen “har haft en stimulerande effekt på bilderboken som föremål”, skriver hon. “När boken-som-medium är under omvandling, och dess territorium hotat, är det som om författarna mer än tidigare utforskar uttrycksformens gränser.” Tidigare hette det ofta att denna tendens handlade om en sorts analog nostalgi – vilket väl fortsatt titt som tätt är fallet, exempelvis beträffande Quentin Tarantinos nya film, The Hateful Eight, inspelad på 70 mm vintage-celluloid.
Men jag undrar om detta koncept inte snarare borde föras över mot resonemang kring ett slags övergripande post-analogitet som tar spjärn mot “det digitala”. Den revival som vinyl-skivor upplever sorterar in här, men boken är förmodligen den medieform där en sam- och framtida post-analogitet framträder som allra tydligast. Borg skriver mot slutet av sin text att “fenomenet kan relateras till två andra postdigitala, mediestrategiska tendenser: storytelling och så kallat 360-gradigt berättande.” Det stämmer – men bara delvis. Dels tror jag man bör överge det post-digitala som begrepp och beskrivning av analoga medieformer, dels är dessa bägge tendenser tydliga även i helt andra mediesammanhang, som exempelvis dataspel och den vertikala integration som länge präglat Hollywood och som nu är legio inom såväl spel- som filmbranschen. Olika slags pågående omförhandlingar kring boken (och dess materialitet) som medium bör snarare betecknas som post-analogt – och här är det givetvis också intressant att fundera på vilka medieformer som inte tar sig den här typen av uttryck, televisionen framför allt. Post-analog tv finns ju inte på kartan (även om det linjära tittandet fortfarande är omfattande).
During autumn I have been working as a guest professor at Södertörn university, within a project run by my colleague Lars Degerstedt at the School of Natural Science, Technology and Environmental Studies. Together we are now writing an article which deals with new forms of media intelligence, and different challenges for the competitive intelligence business. The idea is to have a finished article in mid January and submitting it to Nordicom Review. At present the article has the title: “More Media, More People—Conceptual Challenges for Social and Multimodal Data Driven Competitive Intelligence”. The introduction gives some hints of what we are trying to do, and the text starts like this:
Today, the amount of data produced in a single minute is mind-numbing. Streams—if not floods—of social and multimodal data consequently pose a pivotal challenge for companies within the competitive intelligence business. One of these, the computer software company Domo, has marketed itself as a service designed to provide direct and simplified, real time access to business data without IT involvement. According to Domo, the contemporary data deluge shows no sign of slowing down. “Data Never Sleeps” has hence been the appropriate title of a series of infographics the company has released. The latest version 3.0 was presented in August 2015. Much of what we do every day happens in the digital realm, Domo states. These activities leave an ever increasing digital t®ail “that can be measured and analysed”. Correspondingly, the infographic “Data Never Sleeps 3.0” revealed that every minute users liked a staggering 4,166,667 posts on Facebook, 347,222 tweets were sent on Twitter, at Netflix 77,160 hours of video were streamed every minute—and 300 hours of video uploaded on YouTube. Furthermore, 284,722 images were shared on Snapchat, and at Apple 51,000 apps were downloaded. Notably, these social data transactions occurred every minute, around the clock (Domo, 2015).
Sleepless data hence seems to be the perfect description of today’s global information landscape. Crowd or community based social media, in short, produces data flows that are both a blessing and a curse for competitive intelligence businesses. Handling new forms of social and multimodal data, however, requires new skills—conceptually as well as technologically. However, no data is error-free. On the contrary. There are a number of myths that flourish within the contemporary hype of Big Data. So called data cleansing for example, always has to be performed before, say the depicted data in Domo’s infographic can be analysed. Moreover, the same data also has to be interpreted. All forms of information and media management within the competitive intelligence business basically follows the same pattern: data needs to be collected, entered, compiled, stored, processed, mined, and interpreted. And, importantly: ”the final term in this sequence—interpretation—haunts its predecessors”, as Lisa Gitelman has stressed in the aptly titled book, Raw Data is an Oxymoron (Gitelman, 2013, 3).
With “each click, share and like, the world’s data pool is expanding faster than we comprehend”, the Domo infographic informs potential customers. At a Domo event prior to the launch of the infographic 3.0, the data artist—yes, that is the way he describes himself—Jer Thorp, stated that “not only are we doing more with data, data is doing more with us”. For consumers and business users alike, “improving our lives” thus requires a better understanding of what contemporary “interactions with data” actually mean, according to both Thorp (and Domo). And naturally this is exactly what is being marketed: only Domo can help a business make sense of the “endless stream of data”. The company even has a business intelligence tool with the enticing name “Magic”, that lets customers “cleanse, combine and transform” their data. Data combinatorics provides greater insights, Domo asserts, and thus enables customers to see the whole picture. “Magic provides several intuitive tools to help you prepare your data”—and especially so, if Magic is combined with the company’s presentational tool kit that “quickly interprets the data for you, and suggests how to visualize it for maximum impact and clarity” (Domo, 2015). In other words, the infographic of Domo is aesthetically pleasing for a reason. Today within the competitive intelligence business, maximum impact simply requires Beatiful Data—which happens to be the title of a fascinating book by Orit Halpern. According to her, all data “must be crafted and mined in order to [become] valuable and beautiful” (Halpern, 2014, 5).
Domo is in many ways a successfull American start-up, currently funded by venture capital, but also with a cristal clear business plan. In a video demo, Domo state that their core idea revolves around “the future of business management”. The demo gives viewers an “exclusive look at Domo”, ending with the invocation: “what you need is a platform that brings your people and all the data they rely on together in one place.” In short, Domo is all about business intelligence as social data. Via this video demo, the beautiful infographic and the sleepless data presented by Domo, the purpose of this article is to address similar challenges facing competitive intelligence in an a gradually modified information landscape. When data structures information—what to collect and analyse? If Domo promises it’s customers that their platform makes it “easy to see the information you care about”, how is data perceive and conceptualised? (Domo, 2015). In this article, we argue that data driven competitive intelligence—which is basically what companies like Domo do—particularly needs to pay attention to new forms of (A.) crowd orientated and (B.) media saturated information. If business intelligence traditionally has referred to a set of techniques and tools that transforms textual data into useful information for business analysis, such techniques need to consider that the media landscape has been altered in both a social and non textual direction.
If more data—is better data (as some would have it), accordingly more people that create more media, should be understood in a similar way. This article will consequently start with some introductory remarks around the broader concept of “media intelligence”, and the ways that competitive intelligence businesses has adapted to a transformed media environment—turned datascape. In the subsequent sections, the notions of “social competitive intelligence” and “media analytics” are used as two further concepts that media intelligence evolve around. Firstly, social competitive intelligence tries to understand how a changing information environment will impact organizations and companies by monitoring events, actors and trends. Information today doesn’t only want to be free—information wants to be social. If general usage of technology was once described with terms like social engineering, the linchpin of today’s culture of connectivity is social software. By presenting some findings from the so called CIBAS-project, we thus describe how organisations and companies increasingly rely on (more or less) (in)formal social networking structures and individual decision making as a means to increase rapid response and agile creativity. Secondly, if business analytics focuses on developing insights primarily on textual data and statistical methods, media analytics basically does the same—yet giving priority to audiovisual media streams, often with a slant of sociality—so called social video is for example perceived as increasingly trendy in the way businesses will use social media in years ahead. In our article we use “fashion analytics” as an example, gleaned from a commercial sector where audiovisual big data is currently in vogue. Finally, some concluding remarks are presented.
Jag har idag publicerat en text på DN Kultur om ‘arkivets’ olika datalogis riktningar – I den digitala bildningen kan framtiden ersätta historien. Det hela är ett försök att fundera kring digital bildning; ingressen ger en ungefärlig bild av artikeln: “Med digitaliseringen och insamlingen av information har kunskapsbanker vuxit fram som en ny sorts kulturbas och kulturarv. Pelle Snickars visar hur big data kommer att påverka vår syn på bildning i framtiden.”
Jag har idag publicerat en kommentar i DN Kultur, Medieutredningens censur av min artikel är besynnerlig, med anledning av att en text jag skrivit för Medieutredningens kommande forskningsantologi plockats bort – och detta med hänvisning till att jag sitter med i mediebranschens public service-kommission. Det är aldrig trevligt med censur.
Tillägg – det blev under dagen en del diskussion om mitt inlägg. Vilket fick till följd att både Mittmedia och SR ville publicera den censurerade artikeln i sin helhet. SR skriver exempelvis om det hela här, Därför publicerar Medieormen Pelle Snickars artikel som stoppades av Medieutredningen. Min artikel har titeln, “Personifierad data, informationskonsumism och datahandlare. Inför en grön datahushållning” och är en text på 9 000 ord. Den har nu publicerats på Mittmedia här, Inför en grön datahushållning – och på Sveriges Radios Medieormen-webb här, Pelle Snickars om personifierad data, informationskonsumism och datahandlare. I det senare fallet handlar det alltså om följande: en artikel blir censurerad av en statlig medieutredning på grund av att författaren sitter med i påstådd anti-public-service-kommission – varefter texten publiceras av just public service. En medial ironi om något.
Idag lanserar vi boken Massmedieproblem. Mediestudiets formering. Jag har redigerat den tillsammans med Mats Hyvönen och Per Vesterlund. Det är min sjuttonde bokpublikation. Följer man baksidan handlar den om följande:
Under 1960-talet blev begreppet massmedia ett nytt modeord i svenskt samhällsliv. Med televisionens intåg, populärkulturens uppsving, och häftiga debatter runt filmpolitiken kom behovet av kunskap om medier att framstå som akut. De så kallade massmedieproblemen turnerades ständigt i den allmänna diskussionen.
Boken Massmedieproblem – mediestudiets formering handlar om hur den svenska medieforskningen etablerades. Det skedde i hög grad i ett tämligen diffust gränsland mellan bransch, politik och akademi – detta är en av bokens centrala tankar. De expanderande massmedierna tycktes erbjuda såväl löften som ständiga problem. Perioden mellan 1960 och 1980 var en tid som utmärktes av en stark tilltro till politiska lösningar på olika samhällsproblem, och den gryende medievetenskapen och det offentliga utredningsväsendet kom därför tidigt att vävas samman. I boken fokuserar forskare ur flera generationer och från olika discipliner den svenska kunskapsproduktionen kring medier under 1960- och 1970-talen. Historien kastar emellertid långa skuggor framåt i tiden; den svenska medievetenskapens förflutna präglar fortsatt mediestudiets formering. Om 1960-talets medieforskning drevs fram i en tidsanda – artikulerad i samhällsdebatt och i samhällsstyrning av medier i samverkan med akademi – är frågan hur det egentligen är ställt numera. Går dagens medieforskning i takt med sin tid?
Boken är CC-licensierad och kan laddas ned här.
The research project “Streaming Heritage: ‘Following Files’ in Digital Music Distribution”, funded by the Swedish Research Council, is now in its second year. The project team consists of Pelle Snickars (project leader), Rasmus Fleischer, Anna Johansson, Patrick Vonderau and Maria Eriksson. The project is located at HUMlab where developers Roger Mähler and Fredrik Palm do the actual coding. In short, the project studies emerging streaming media cultures in general, and the music service Spotify in particular (with a bearing on the digital challenges posed by direct access to musical heritage.) Building on the tradition of ‘breaching experiments’ in ethnomethodology, the research group seeks to break into the hidden infrastructures of digital music distribution in order to study its underlying norms and structures. The key idea is to ‘follow files’ (rather than the people making or using them) on their distributive journey through the streaming ecosystem.
So far research has focused basically four broader areas: the history and evolvement of streaming music in general and Spotify in particular (Fleischer), streaming aggregation’s politics and effects on value and cultural production (Vonderau), the tracing of historical development of music metadata management and its ties to knowledge production and management that falls under the headline of ‘big data’ (Eriksson), and various forms of bot culture in relation to automated music aggregation (Snickars). One article has been published, and more preliminary results are to be presented in a number of upcoming articles and conferences during 2016. Eriksson, for example recently submitted an article around digital music distribution increasingly powered by automated mechanisms that capture, sort and analyze large amounts of web-based data. The article traces the historical development of music metadata management and its ties to the field of ‘big data’ knowledge production. In particular, it explores the data catching mechanisms enabled by the Spotify-owned company The Echo Nest, and provides a close reading of parts of the company’s collection and analysis of data regarding musicians. In a similar manner, Johansson and Eriksson are exploring how music recommendations are entangled with fantasies of for example age, gender, and geography. By capturing and analyzing the music recommendations Spotify delivers to a selected number of pre-designed Spotify users, the experiment sets out to explore how the Spotify client, and it’s algorithms, are performative of user identities and taste constellations. Results will be presented at various conferences during next year. In addition, Snickars has continued working with the HUMlab programers on various forms of “bot experiments”. One forthcoming article focuses the streaming notion of “more music”, and an abstract for the upcoming DH-conference in Kraków (during the summer of 2016) is entitled: “SpotiBot—Turing testing Spotify”. It reads as follows, and gives an indication of the ways in which the project is being conducted:
Under the computational hood of streaming services all streams are equal, and every stream thus means (potentially) increased revenue from advertisers. Spotify is hence likely to include—rather than reject—various forms of (semi-)automated music, sounds and (audio) bots. At HUMlab we therefore set up an experiment—SpotiBot—with the purpose to determine if it was possible to provoke, or even to some extent undermine, the Spotify business model (based on the 30 second royalty rule). Royalties from Spotify are only disbursed once a song is registered as a play, which happens after 30 seconds. The SpotiBot engine was be used to play a single track repeatedly (both self-produced music and Abba’s ”Dancing Queen”), during less and more than 30 seconds, and with a fixed repetition scheme running from 10 to n times, simultaneously with different Spotify account. Based on a set of tools provided by Selenium the SpotiBot engine automated the Spotify web client by simulating user interaction within the web interface. From a computational perspective the Spotify web client appeared as black box; the logics that the Spotify application was governed by was, for example, not known in advance, and the web page structure (in HTML) and client side scripting quite complex. It was not doable within the experiment to gain a fuller understanding of the dialogue between the client and the server. As a consequence, the development of the SpotiBot-experiment was (to some extent) based on ‘trial and error’ how the client behaved, and what kind of data was sent from the server for different user actions. Using a single virtual machine—hidden behind only one proxy IP—the results nevertheless indicate that it is possible to automatically play tracks for thousands of repetitions that exceeds the royalty rule. Even if we encountered a number of problems and deviations that interrupted the client execution, the Spotify business model can in short be tampered with. In other words, one might ask what happens when—not if—streaming bots approximate human listener behavior in such a way that it becomes impossible to distinguish between a human and a machine? Streaming fraud, as it has been labeled, then runs the risk of undermining the economic revenue models of streaming services as Spotify.
Finally, during the following weeks the project group will do presentations in the U.S. The first one is called, “Spotify Teardown”, and consists of a project presentation and roundtable at the Center for Information Technology and Society at the University of California, Santa Barbara. On the one hand the presentation will have a focus on methodology, background research and preliminary findings, and on the other hand try to initiate a discussion with three focused areas: (1.) ”Ethical and Legal Limitations”: What are the ethical/legal issues that arise in relation to activist projects, and how to tackle them? (2.) ”Metaphors for Research”: What metaphors are useful, or more useful than conventional metaphors such as “platform” or “platform responsibility”? and (3.) ”New Qualitative Methods and Old Disciplinary Frameworks”: What are the key challenges of working with qualitative, inter- and transdisciplinary methods in institutional environments? In addition, Pelle Snickars will also do another project presentation in New York at Cuny (The City University of New York) at the conference, ”Digging Deep: Ecosystems, Institutions and Processes for Critical Making”.
Idag intervjuas jag i Göteborgs-Posten kring vad jag lite löst kallar för “den automatiserade offentligheten”. “Att du får mindre och ny typ av spam innebär inte att de digitala reklambudskapen minskar. Avsändarna agerar allt mer i det fördolda”, står det i ingressen. Intervjun är tämligen kort – och kan läsas här: Robotar tar över reklamen.
Idag recenserar jag Andrew Keens nya bok, Internet är inte svaret i DN; inleder så här: “I webbens barndom gjorde science fictionförfattaren William Gibson prognosen att framtiden redan är här – ”den är bara inte särskilt jämt fördelad”. Det är en stilig oneliner, och fungerar som ett slags motto för Andrew Keens ny bok. Som författare och it-entreprenör har han under det senaste decenniet gjort internationell karriär som den kanske mest oförsonlige nätskeptikern av alla.” Recensionen kan läsas här: Vem ska tämja internet?.
Idag har jag föreläst på konsthögskolan kring vad jag lätt spekulativt kallat publiceringsfigurens död. Tanken var att använda termen ”publicering” som ett plastiskt och dynamiskt begrepp vilket i allra högsta grad kan användas för att förstå ”det digitala”. I föreläsningen turnerar jag därför begreppet ”digital publicering” – förstått i synnerligen vid bemärkelse – från tre olika mediala perspektiv vilka alla hänger samman med medieforskning som jag ägnat mig åt, eller som jag för närvarande arbetar med: 1. Multimedial publicering & remix, 2. Illegal publicering som bevarande, och 3. Publicering som explorativ och undersökande metod. Föreläsningen kan laddas ned som PDF här: snickars_konsthögskolan_2015.