Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Business models of micro businesses: Empirical evidence from creative industries

Business model describes how a business identifies and creates value for customers and how it organizes itself to capture some of this value in a profitable manner. Previous studies of business models in creative industries have only recently identified the unresolved issues in this field of research. The main objective of this article is to analyse the structure and diversity of business models and to deduce how these components interact or change in the context of micro and small businesses in creative services such as advertising, architecture and design. The article uses a qualitative approach. Case studies and semi-structured, in-depth interviews with six owners/managers of micro businesses in Croatia provide rich data. Structural coding in data analysis has been performed manually. The qualitative analysis has indicative relevance for the assessment and comparison of business models, however, it provides insights into which components of business models seem to be consolidated and which seem to contribute to the diversity of business models in creative industries. The article contributes to the advancement of empirical evidence and conceptual constructs that might lead to more advanced methodological approaches and proposition of the core typologies or classifications of business models in creative industries. In addition, a more detailed mapping of different choices available in managing value creation, value capturing or value networking might be a valuable help for owners/managers who want to change or cross-fertilize their business models

Business models of micro businesses: Empirical evidence from creative industries

Business model describes how a business identifies and creates value for customers and how it organizes itself to capture some of this value in a profitable manner. Previous studies of business models in creative industries have only recently identified the unresolved issues in this field of research. The main objective of this article is to analyse the structure and diversity of business models and to deduce how these components interact or change in the context of micro and small businesses in creative services such as advertising, architecture and design. The article uses a qualitative approach. Case studies and semi-structured, in-depth interviews with six owners/managers of micro businesses in Croatia provide rich data. Structural coding in data analysis has been performed manually. The qualitative analysis has indicative relevance for the assessment and comparison of business models, however, it provides insights into which components of business models seem to be consolidated and which seem to contribute to the diversity of business models in creative industries. The article contributes to the advancement of empirical evidence and conceptual constructs that might lead to more advanced methodological approaches and proposition of the core typologies or classifications of business models in creative industries. In addition, a more detailed mapping of different choices available in managing value creation, value capturing or value networking might be a valuable help for owners/managers who want to change or cross-fertilize their business models

The similarity metric

A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new ``normalized information distance'', based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the {\em similarity metric}. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.Comment: 13 pages, LaTex, 5 figures, Part of this work appeared in Proc. 14th ACM-SIAM Symp. Discrete Algorithms, 2003. This is the final, corrected, version to appear in IEEE Trans Inform. T

Uloga društvenih medija u mjerenju TV gledanosti

For many decades, traditional broadcast has been the main entertainment focal point in households. Like all media and entertainment industries, television has been altered by the internet and new technologies. The internet has made new forms of participatory communication possible and has increased the amount of interpersonal communication for individuals – audiences and users – providing opportunities to share, create and collaborate together. It offers manifold opportunities to communicate in all directions, as well as the opportunity to transmit and receive simultaneously all kinds of content and formats such as music, films, pictures and texts and enables the user to interact with links. The development of social media is more than a technical innovation: it sustains and influences all forms of social organisations. Besides (high speed) internet itself, wireless connectivity has created a comfortable environment for the usage of different devices. Smartphones, tablets and/or laptops are conquering households and invite (connected) usage while people watch TV; audiences divide their attention between a second and first screen, becoming a user and audience at the same time. It enables participation and social interaction within social media while watching TV. “Actions of the participatory audience appear in the value chain in several phases: when the audience is creating content, when they are editing or reediting the available content and when they are disseminating the content to other audience members” (Noguera Vivo et al., 2014, p. 181). This new participation of TV audiences in social media leads to an integration of TV consumption within the social media context. The “people formerly known as the audience are those who were on the receiving end of a media system that ran one way, in a broadcasting pattern, with high entry fees and a few firms competing to speak very loudly while the rest of the population listened in isolation from one another […]” (Rosen, 2006), the audience transformed into an active audience participating in the creation of (social) media content. The second screens enable virtual communication with friends about programs while watching and sharing what is liked and disliked, and television viewing coupled with audience interaction has gained popularity (Doughty, Rowland and Lawson, 2011). The audience can share, discuss, comment and vote about certain programs. Broadcasters and other suppliers offer applications accompanying TV consumption and solicit simultaneous usage. Audiences engage with the program and socialise with friends and communities around their favourite content. Television audience researchers discovered the internet as a source of audience data, and search for approaches to analyse online engagement of audiences. The main question of this work is to investigate if new data can be found and used in a systematic manner in addition to traditional television audience research methods. It was discovered that the relationship between television broadcasters and its social audience is the key to this approach. Traditional media such as TV broadcasters are still huge content providers and play a major role in the social media world, where content is shared and creates buzz and in addition users generate content themselves. Broadcasters are challenged to keep the relationship with and the attention of the viewer by building social interaction around the program. This is the prerequisite for the researcher to approach social media analysis in the context of television.Tradicionalno emitiranje je već desetljećima žarište zabave u kućanstvima. Kao i svi ostali mediji te industrije zabave, televizija je promijenjena zahvaljujući internetu i novim tehnologijama. Internet je omogućio nove forme komunikacije između sudionika te je povećao broj međuljutske komunikacije za pojedince – gledatelje i korisnike – tako što je omogućio prilike za zajedničkim dijeljenjem, stvaranjem i surađivanjem. Pruža mnoge prilike za komunikaciju u svim smjerovima, jednako kao i priliku za simultano slanje i primanje raznih vrsta sadržaja te formata kao što su muzika, filmovi, slike i tekstovi. Također omogućuje korisniku interakciju s web linkovima. Razvoj društvenih medija je više od tehnološke inovacije, ono podržava i utječe na sve oblike društvenih organizacija. Pored toga, sam internet (velike brzine) je uz bežično spajanje stvorio ugodnu okolinu za korištenje raznih uređaja. Pametni telefoni, tableti, i/ili laptopovi osvajaju kućanstva te pozivaju korisnika na online spajanje i korištenje interneta za vrijeme gledanja televizije pa tako gledatelji dijele svoju pozornost između dva ekrana, postajući na taj način istovremeni korisnici i gledatelji. Ovo omogućuje sudjelovanje te društvenu interakciju unutar društvenih medija tijekom gledanja televizije. “Djela uključenih gledatelja se pojavljuju u lancu vrijednosti u nekoliko faza: kada gledatelji stvaraju sadržaj, kada uređuju ili preuređuju dostupan sadržaj, te kada šire sadržaj drugim gledateljima. ” (Noguera Vivo et al., 2014, p. 181). Ovo novo sudjelovanje gledatelja na društvenim medijima vodi k integraciji gledanja televizije unutar konteksta društvenih medija. “Ljudi koji su prethodno prepoznati kao gledatelji su bili na primajućem kraju medijskog sustava koji se kretao u jednom smjeru, po strukturi emitiranja, uz visoke članarine te nekoliko tvrtki koje se natječu u tome da govore što glasnije dok ostatak populacije sluša u međusobnoj izolaciji […]” (Rosen, 2006), gledatelji su se pretvorili u aktivne gledatelje koji sudjeluju u stvaranju sadržaja (društvenih) medija. “Dodatni zasloni omogućavaju virtualnu komunikaciju s prijateljima o TV programima tijekom gledanja i dijeljenja sadržaja koji im se sviđa, odnosno ne sviđa, a i samo gledanje televizije s istovremenom interakcijom gledatelja postaje sve popularnije.” (Doughty, Rowland and Lawson, 2011). Gledatelji mogu dijeliti sadržaj, raspravljati, komentirati te glasati za određene TV programe. Televizijske kuće i ostali dobavljači nude aplikacije za praćenje korištenja usluge televizije te potiču njezino simultano korištenje. Gledatelji se uključuju u TV programe te raspravljaju s prijateljima i raznim zajednicama o njihovim najdražim sadržajima. Istražitelji koji prate gledanost televizije su prepoznali internet kao izvor podataka o gledateljima te istražuju pristupe za analizu online angažiranosti gledatelja. Potrebno je istražiti mogu li se pronaći novi podaci koji se mogu iskoristiti na sustavan način uz tradicionalne metode istraživanja gledanosti televizije. Otkriveno je da je ključ ovom pristupu sam odnos između televizijskih kuća i njihove publike, odnosno gledatelja. Tradicionalni mediji kao što su televizijske kuće se i dalje smatraju značajnim pružateljima sadržaja te igraju važnu ulogu u svijetu društvenih medija, gdje se dijele sadržaji koji stvaraju vijesti, i sadržaj pružaju sami korisnici. Izazov televizijskih kuća je da održavaju odnos s gledateljima te da imaju njihovu pozornost tako što će izgraditi društvenu interakciju oko TV programa. Ovo je preduvjet istražiteljima kako bi pristupili analizi društvenih medija u kontekstu televizije