Epävarmuudesta arvokkaaseen lopputulokseen - serendipisyys tiedonhankintakäyttäytymisessä

A serendipitious event in everyday life is common: it means unexpected information that yields some unintended information and potential value later on. Serendipity as a word has been around for hundreds of years. As a studied concept it is rather recent. Serendipity is not just the unexpected information or experience but rather the ability to recognize and do something with it. Serendipitious discovery of information is different from purposive or known item search as it is more complicated and lasts much longer. The discovery of information by chance or accident is still looking it’s explicit place in models and frameworks of information behaviour. It is still not clear what constitutes the core of the research area of serendipity in information behaviour. The qualities of interaction among people, information, and objects differ in physical vs. digital environments. The bisociation, a creative association between different peaces of information may be computer supported. This article presents an overview of the research study of serendipity in information seeking behaviour. We explore serendipity mainly in the digital information environment. As a setting for our study we use six main drivers of serendipity research relating to digital enviroments presented in McCay-Peet and Toms (2017). The drivers are: 1. Theoretical understanding of the phenomen of serendipity, 2) physical vs digital, 3) information overload, 4) filter bubbles, 5) user experience, and 6) user strategies. A new refined temporal model of information encountering by Erdelez and Makri (2020) is also presented in this article. The model presents a framework for better understanding of the temporal dimension of the information acqusition. At a macro level the model positions information encountering within contextual factors related for user, information, task and environment related characteristics.

IPR-aineistojen mahdollisuuksia, replikointitarpeita ja teknisiä haasteita

Tässä artikkelissa tehdään katsaus aineettomien oikeuksien eli IPR- (intellectual property rights) aineistoihin erityisesti suomalaisesta ja eurooppalaisesta näkökulmasta. Keskityn patentti-, hyödyllisyysmalli-, tavaramerkki- ja mallioikeusaineistoihin eli teollisoikeuksiin jättäen tekijänoikeudet tarkastelun ulkopuolelle. Digitalisaation myötä rekisteriaineistojen laatu ja saatavuus ovat jatkuvasti lisääntyneet ja IPR-järjestelmien toiminnan tutkimus sekä hypoteesien empiirinen testaus ovat tulleet mahdolliseksi ennennäkemättömällä tavalla. Eräitä mielenkiintoisia aiheita, joiden tutkimuksen yhä laadukkaammat IPR-aineistot mahdollistavat ovat 1) kokonaisvaltaisempi IPR-analyysi, joka patenttien ohella hyödyntää tavaramerkki-, mallioikeus- ja hyödyllisyysmalliaineistoja, 2) Euroopan integraation ja Kiinan roolin kasvun vaikutusten analyysi IPR-kontekstissa ja 3) digitalisaation ja tekoälyn vaikutusten arviointi sekä uudet sovelluskohteet IPR-alalla. Lisäksi aiempien tutkimusten toistaminen eli replikointi nykyisillä aiempaa kattavammilla ja laadukkaammilla rekisteriaineistoilla olisi tärkeää, koska digitalisaatio ja globalisaatio ovat 2000-luvulla muuttaneet IPR-toimintaympäristöä nopealla tahdilla.

Bridging the gap between critical algorithm studies and the social sciences

Since the mid-2000s platforms and platform work have greatly proliferated. Although also research on platforms and platforms has expanded, there seems to be a great gulf between IT- and social research on these issues. In this article, I show, using sociologist Maurizio Lazzarato’s concepts, that the technical infrastructure of the platform, in particular the algorithm, has a direct impact on work and the agency of the platform worker. From this perspective, it is possible to ask critical questions about platforms and platform work from a multidisciplinary viewpoint. Methodologically, an ethnographic approach that sees algorithms as part of culture, with its own institutions, impact on people and intersection with different contexts, could support co-operation between different disciplines. When such an approach is combined with insights from algorithm studies, research is also relevant for existing political questions regarding platform workers’ labour market status and the functioning of platform firms.

Sosiaalisen median kommenttien analyysi multimodaalisesta näkökulmasta digitaalisen journalismin ja poliittisen viestinnän tutkimuksessa

Societal discussions flow on social media platforms that are studied by researchers in multiple ways and through various kinds of data sets that are extracted from them. In the studies of these discussions, multimodality unravels the semiotic modes that are communication resources through which meanings are socially and culturally created and expressed. In addition, the viewpoint of affordances can be used for viewing the functions of social media platforms and their discussions. Furthermore, this review was conducted to better understand how social media comments are researched from the perspective of multimodality in the context of digital journalism and political communication. A systematic literature review and qualitative content analysis were used as methods. The review discovered that the studies under review were not that high in multimodality and that text as an individual mode was the most common one. Furthermore, Twitter was the most researched platform and the one where the use of modes was more thoroughly explained.

Kannanottaminen Twitterissä sosiaalisen ja digitaalisen toiminnan rajapintana

This study explores assessing as a social and digital activity in Twitter interactions on remote work. Digital interaction analysis of progressivity and sequentiality is used as a method that is based on online discussion research and applied conversation analysis. In hashtag #etätyö discussion participants are showing appreciation for the opportunity to work remotely, evaluating features of remote work, and advising remote workers through assessing forms and actions. Simultaneously, they progressively carry on and orient to the specific ‘hashtagged’ lines of discussion. In specific thread discussion participants organize assessing activity through positive and complementary, and occasionally negative, responses that are reciprocally and sequentially produced in line with the thread’s first tweet doing positive assessment. In conclusion, societally important issues should be assessed using jointly social and digital affordances available in the platform contexts. Furthermore, the study contributes to the discussion in digital humanities and social sciences on the importance of social interaction perspective.

Improving candidate-based voting advice application design

Voting advice applications (VAAs) seek to strengthen democracy by assisting voters in voting-related decision-making. VAAs have become popular tools across many European democracies and their usage has been linked to real life electoral consequences. As VAA usage has become more prominent, this has also sparked research interest toward VAA design. However, most of the VAA design research has been conducted on party-based VAAs, whereas candidate-based VAAs that are in use in countries such as Finland, Denmark and Switzerland have been largely ignored. Moreover, research on overall VAA designs, in comparison to individual VAA design elements, is non-existent. This article discusses interdependencies of VAA design choices and proposes overall VAA designs that can improve existing Finnish candidate-based VAA designs, encouraging Finnish VAA developers to update their designs. Future research should develop and test the suggested designs, as more research on the topic is needed, especially regarding VAA user experiences.

Aihemallinnuksen ja klusterianalyysin yhdistäminen aineiston esikäsittelyn ja mallinnuksen valintojen tutkimiseksi

Uusien tekstiaineistojen analyysimenetelmien käytön yhteiskuntatieteelliset käytännöt eivät ole vielä vakiintuneet. Yksi suosittu keskustelu- ja tekstiaineistojen mallinnustapa on aihemallinnus, jolla etsitään aineiston temaattista rakennetta sanojen yhteisesiintymisen avulla.Aihemallinnuksen tulokset vaihtelevat aineiston esikäsittelyn ja mallinnuksen parametrien myötä, ja työkalusta riippuen myös satunnaisesti. Tämä on yleensä tulkittu ongelmaksi, josta päästään eroon huolellisesti validoimalla ja valitsemalla yksi ”paras malli”. Sosiaalitieteilijän näkökulmasta mallinnuksen vaihtelut voivat kuitenkin olla myös erilaisia näkökulmia aineistoon tai vivahde-eroja, joita tulkitsemalla voidaan löytää aineiston ydin.Tässä artikkelissa käsitellään tutkimusprosessia, joka perustuu toistettuihin aihemallinnuksiin aineiston esivalmisteluja ja mallinnuksen parametreja vaihtelemalla. Kahden aiheen samankaltaisuus voidaan mitata ja lukuisista malleista tuotetut aiheet voidaan ryhmitellä klusterianalyysilla näiden samankaltaisuuksien avulla. Kun kaksi aihetta sijoittuu samaan ryhmään, voidaan niiden tulkita olennaisesti kuvaavan samaa aihetta, vaikka sanajakauma ei olekaan täysin sama. Nämä aiheiden ryhmät voidaan sitten nostaa analyysin keskiöön: jotkin aiheet löytyvät riippumatta alkuvalmisteluista, jotkin vain toisinaan ja jotkut aiheet löytyvät vain sattumalta ja jäävät yksin. Yhden mallin tulkinnan rinnalla ja sijasta voidaan tulkita näitä ryhmiä, ja samalla tehdä näkyväksi mallin reliabiliteettia ja tehtyjen valintojen vaikutusta tuloksiin.