Social media has become an important tool for communication and marketing, and proper use of visual and textual information is very influential in social media marketing. Research has gone a long way explaining how information content significantly influences decision-making, however, it still lacks the knowledge about how different forms of information (such as textual, visual and audio-visual) in social media affects decision-making.  The aim of this study was to identify the use of social media by international students for choosing study abroad destinations, as well as the forms of information content that have a greater influence in their decision-making process. To achieve the aim, a qualitative approach was applied to collect data through semi-structured interviews with fourteen international master students at Stockholm Business School in Sweden. This study shows that social media has a low influence on international students’ decision-making with regards to the choice of study destination; however, they use social media as a search tool to conceptualise and justify their choice, feel stronger about their decision, and to increase their confidence. This study suggests that social media could indirectly or subconsciously play a part in students’ choice of study destination as the students could be subconsciously affected by social media information, in particular, by visual and audio-visual information. Moreover, information contents such as videos that are more provocative, based on multisensory and emotional cues, could have a greater influence on the international student. Additionally, international students experience higher levels of trust when they feel that the content is authentic. Finally, the thesis concludes with theoretical implications and recommendations for further research.

年報中富含眾多資訊，其中包含財務性資訊與文字性資訊，財務性資訊之分析方法已相當成熟，而文字性資訊受限於格式及檔案類型，而降低投資人使用或分析此類資訊之效率。管理階層討論與分析(Management’s Discussion & Analysis of Financial Condition and Results of Operations，以下簡稱MD&A)係管理階層傳達其經營決策觀點予投資人之媒介，投資人可透過閱讀MD&A取得更多資訊，過去學者之研究亦證實該項目內之文字性資訊有其重要性，由於文字性資訊缺乏通用之分類架構，因此投資人需耗費較多時間與成本分析該資訊。本研究自美國科技業上市公司，隨機選取40家企業2012年之年報作為樣本資料，藉由文字探勘技術，運用TFIDF將MD&A文字性內容分類至EBRC針對MD&A所發布之分類架構，建立分類閱讀器，使投資人可利用透過系統分類並彙整之文句，迅速取得所需之文字性資訊，以協助使用者有效率地閱讀這些非結構化之文字資訊，藉以減少資料蒐集之時間，提升文字性資訊之可使用性。 / Annual reports are rich in information, which contains financial information and textual information. While the approach of analyzing financial information is common, textual information is confined by its format or the file type it is stored, thus decreasing the efficiency of analyzing this sort of information. Management’s Discussion & Analysis of Financial Condition and Results of Operations (MD&A) is the vehicle for investor to share the sight of managements’ decision making consideration, through reading MD&A investor could obtain more information. According to past researches, textual information is of importance. Due to the lack of a common framework, investors would consume more time and cost to analyze textual information. This research randomly selected 40 samples from publicly traded technology firms of the United-States. Utilizing text-mining technology and TFIDF, classify textual information of MD&A into the framework EBRC established, developing a classified reader for MD&A. To assist investors read non-constructed textual information efficiently and reduce the time of information gathering, thereby enhancing the usability of textual information.

<p>A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions.</p><p>A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.</p> / IPOS, PROEK

A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option. / IPOS, PROEK

本研究蒐集經濟日報、聯合報、與聯合晚報的新聞文章，以中研院的中文斷詞性統進 行結構性的處理，參考並延伸Tetlock、Saar-Tsechansky和Macskassy(2008)的研究方法，檢驗 使用一個簡單的語言量化方式是否能夠用來解釋與預測個別公司的會計營收與股票報酬。有 以下發現： 1. 正面詞彙(褒義詞)在新聞報導中的比例能夠預測高的公司營收。 2. 公司的股價對負面詞彙(貶義詞)有過度反應的現象，對正面詞彙(褒義詞)則有效率地充分 反應。 綜合以上發現，本論文得到，新聞媒體的文字內容能夠捕捉到一些關於公司基本面難 以量化的部份，而投資者迅速地將這些資訊併入股價。 / This research collects all of the news stories about China Steel Corporation from Economic Daily News, United Daily News, and United Evening News. These articles I collect are segmented by a Chinese Word Segmentation System of Academia Sinica and used by the methodology of Tetlock, Saar-Tsechansky, and Macskassy(2008). I examine whether a simple quantitative measure fo language can be used to predict individual firms’ accounting sales and stock returns. My two main findings are: 1. the fraction of positive words (commendatory term) in firm-specific news stories forecasts high firm sales; 2. firm’s stock prices briefly overreaction to the information embedded in negative words (Derogatory term); on the other hand, firm’s stock prices efficiently incorporate the information embedded in positive words (commendatory term). All of the above, we conclude this linguistic media content captures otherwise hard-toquantify aspects of firms’ fundamentals, which investors quickly incorporate into stock prices.