This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.

Yes / Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

Social media platforms create rich social structures, expand users' boundaries of social networks and revolutionize traditional forms of communications, social interactions and social relationships. These platforms not only facilitate the creation and sharing of news and information, but they also drive various kinds of businesses models, processes and operations, knowledge sharing, marketing strategies for brand management and socio-political discourses essential for healthy and democratic functions. As such, social media has greater implications on organizations and society brought about by individuals' social media usage patterns, and therefore, calls for further investigations. The main objective of this dissertation is to explore and offer insights into such social media usage and information sharing behaviors via data driven examination of various theories. This dissertation involves three studies that focus on factors that explain individuals' three different social media usage behaviors. Essay 1 investigates individuals' perceived importance of online affiliation, self-esteem, self-regulation and risk-benefit structure as antecedents of users' geo-tagging behavior on social media. Essay 2 examines the role of online news quality, source credibility, individuals' perception towards online civic engagement, attitude towards news sharing and social influences to understand users' news sharing behavior on social media platforms. Essay 3 seeks to examine the individuals' information verification behavior on social media through the lens of individuals' fake news awareness, perceived cost of information verification, trust in social media and truth-seeking.

人智運算(Human Computation)是近年來最熱門的研究領域之一，而適地性服務(Location-based service)也因此衍生出許多研究議題。由於傳統上在搜集資訊時往往會浪費過多的人力資源，所以我們希望使用者藉由玩遊戲的方式背後完成我們想要的事情。在此篇論文中，我們利用GWAP(Games with a Purpose)的概念設計一套行動地理標籤系統，系統採用玩家分享自身附近景點資訊再透過其他玩家來到相同地點做驗證的方式，讓玩家在一邊玩遊戲的過程中搜集相關地理資訊，另一方面達到景點資訊是可信賴的目的。 然而，對系統而言要如何選題給玩家驗證才能提升整體效能，為了解決這些問題，我們在選題策略上提出三種不同的任務分配演算法，再透過一連串的實驗模擬來證明我們所設計的系統確實能夠在選題上達到好的效能，最後我們考慮到現實生活中可能的情形後將此系統實作在智慧型手機上(Android Phone)。 / Human Computation is popular recently and have become one of the hottest research topics, therefore Location-based service also drives a lot of issues. Due to collect information always cause too many manpower- wasted in tradition , so we hoped that the users to complete the things which we want by play game’s way .In this thesis, we based on the concept of ‘Games with a Purpose’(GWAP) to develop a mobile geospatial tagging system, system adopt player share self nearby scenic spots information and then through another players come the same place do the verification, allowing the player to play the game in the side of the process of collecting the relevant geographic information, on the other hand to achieve the purpose of scenic spots information is reliable. However, how to choose topics for player verification can effectively evaluate the system performance, to solve these problems we propose three different selection strategies on the task assignment algorithms, then through a series of simulation experiments designed to prove that our system can indeed achieve good performance on the topics, finally we take into account real life situations and implement in the smart phone (Android Phone).

English profanities (i.e. potentially offensive words, including swear words) have been in use for decades in the Nordic languages – Icelandic, Norwegian, Danish, Swedish and Finnish – and offer a multitude of opportunities for linguistic expression, along with the domestic, heritage profanities in each language. The Nordic countries present an interesting context for studying the impact of English on languages in remote-contact settings, where many, especially young people, are bilingual but English has no official status. While previous studies have mostly focused on the function of such words and investigated their appearance in each Nordic language in isolation, this study utilizes social media data from the Nordic Tweet Stream (Laitinen et al., 2018) to compare the forms and frequencies of the English profanities fuck, shit, ass, damn, bitch and hell across the Nordic languages, shedding light on the factors which are conducive to their use. Surprisingly, the English profanities were many times more frequent in the Icelandic material compared to the other languages, although Iceland has a strong tradition of linguistic purism and frequencies were expected to be lower than in the other languages. Contrastingly, the profanities were found to be morphologically and orthographically adapted to a higher degree in Icelandic, reflecting the purist tradition in other ways. Frequencies in the other four languages did not quite match the findings of previous studies on loanwords in the Nordic languages, while degrees of adaptation were more similar to previous results. Comparing the frequencies of the English profanities in this study with the frequencies of heritage profanities on Twitter found by Coats (2021) showed that, although especially fuck and shit are on par with and sometimes more frequent than the most frequent heritage profanities, they do not seem to be replacing domestic equivalents. Finally, through exploiting the geo-location tags that accompany each tweet in the Nordic Tweet Stream, the frequencies of English profanities were found to be higher among users tweeting primarily from large cities in Denmark, Sweden and Finland, while in the Norwegian data no significant difference was found. Nevertheless, this supports Vaattovaara & Peterson’s (2019) claim that English borrowings carry social indices of globalism and urbanicity that promote their use among people in certain social groups. / Engelska fula ord (dvs. potentiellt stötande ord, inklusive svärord) har varit i bruk i årtionden i de nordiska språken – isländska, norska, danska, svenska och finska – och tillhandahåller en stor mängd språkliga uttrycksmöjligheter tillsammans med de inhemska fula orden i varje språk. De nordiska länderna utgör en intressant kontext för studier av engelskans inflytande över andra språk i distans-kontaktsituationer, där många, speciellt unga, är tvåspråkiga emedan engelska saknar officiell status. Då flertalet tidigare studier har fokuserat på denna typ av ords funktion, och undersökt deras förekomst i de nordiska språken var för sig, använder denna studie data från sociala medier, nämligen Nordic Tweet Stream (Laitinen et al., 2018) i en jämförelse av form och frekvens för de engelska fula orden fuck, shit, ass, damn, bitch och hell mellan de nordiska språken, för att därigenom synliggöra faktorer som gynnar deras bruk. Överraskande nog var de engelska fula orden långt mer frekventa i det isländska materialet jämfört med de övriga språken, trots att Island har en stark språkpuristisk tradition och frekvenserna därför förväntades vara lägre än i de andra språken. Däremot uppträdde de fula orden i högre grad i morfologiskt och ortografiskt anpassad form i isländska, vilket påvisar den puristiska traditionen på annat vis. Frekvenserna i de andra fyra språken skiljde sig något från resultaten av tidigare studier av lånord i de nordiska språken, medan anpassningsgraden var mer jämförbar med tidigare studier. En jämförelse av de engelska fula ordens frekvenser i denna studie med inhemska fula ords frekvenser på Twitter i en studie av Coats (2021) visade att medan fuck och shit mäter sig i frekvens med de vanligaste inhemska fula orden, och överträffar dem ibland, verkar de inte ersätta lokala motsvarigheter. Till sist påvisades, genom att utnyttja den geografiska platsdata som åtföljer varje tweet i Nordic Tweet Stream, att de engelska fula ordens frekvenser var högre bland användare som twittrar främst från stora städer i Danmark, Sverige och Finland, medan ingen signifikant skillnad kunde ses i den norska datan. Icke desto mindre styrker detta Vattovaara & Petersons (2019) tes gällande att lån från engelska bär med sig globala och urbana sociala indikationer som främjar deras bruk bland vissa sociala grupper.