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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/158574
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158574


    Title: 無人機操控應用程式之真實性檢視
    Examining the Authenticity of Drone Control Applications
    Authors: 張維暘
    Chang, Wei-Yang
    Contributors: 杜雨儒
    蔡瑞煌

    Tu, Yu-Ju
    Tsaih, Rua-Huan

    張維暘
    Chang, Wei-Yang
    Keywords: 無人機操作應用程式
    官方來源辨識
    自然語言處理
    使用者評論分析
    深度學習
    資訊安全
    訊號理論
    使用者信任
    Drone Control applications
    Official Source Identification
    Natural Language Processing (NLP)
    User Review Analysis
    Deep Learning
    Information Security
    Signaling Theory
    User Trust
    Date: 2025
    Issue Date: 2025-08-04 14:26:47 (UTC+8)
    Abstract: 隨著無人機技術的迅速普及,各類控制應用程式在行動平台上大量湧現,使使用者能夠便利地透過手機操作無人機。然而,在應用商店中,部分標榜特定品牌名稱的應用實際上並非由原廠官方上架,可能造成使用者在不知情的情況下下載未經授權之應用程式,進而引發資訊安全與操作風險。
    為因應此問題,本研究建立一套自動辨識架構,分析使用者對無人機應用程式的評論內容,以判斷該應用是否具有官方來源特徵。研究資料來自公開平台上的用戶評論與相關資訊頁面,並透過語言特徵建構分類模型,辨識應用程式在「官方/非官方」與「真實/假冒」兩種維度上的可能性。此外,本研究亦設計問卷實驗,驗證該分類結果是否能顯著影響使用者對應用程式的信任、下載意願與付費傾向。
    With the rapid proliferation of drone technology, a wide variety of control applications has emerged on mobile platforms, enabling users to operate drones conveniently via smartphones. However, some applications listed on app marketplaces, despite bearing brand-like names, are not officially released by manufacturers. This can result in users unknowingly downloading unauthorized apps, potentially posing information security and operational risks.
    To address this issue, this study proposes an automated identification framework that analyzes user reviews of drone control apps to assess whether the apps exhibit signs of official origin. The dataset is composed of user-generated content and metadata retrieved from public platforms such as Google Play. Based on linguistic features, classification models were trained to estimate the likelihood of an app being “official/unofficial” and “real/fake” across two dimensions.
    Additionally, a user experiment was conducted to evaluate whether these classification outputs significantly influence users’ trust, download intentions, and willingness to pay.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    112356027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112356027
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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