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「假新聞的危機與回應」論壇,發言稿 1,500~3,000字 === ### 自介 嗨大家好,我是 g0v 專案「真的假的 LINE chatbot」的團隊一員 ggm。我們注意到,隨著社交軟體在台灣使用年齡層擴大,社交軟體成為許多假訊息與謠言的傳播溫床,這些謠言可能是錯誤的知識、詐騙、蓄意引導。隨著Google與FB等平台注意到假訊息對社群帶來的危害,在台灣,還有LINE這個廣泛被使用的通訊軟體,也充斥著假訊息,在各個群組之間傳播。 這個專案想解決的問題是降低人們誤信假訊息的機會,透過實做一個LINE機器人,協助使用者判斷訊息真偽,建立一個"只要有疑慮,就來問問 LINE 機器人"的懷疑態度。使用者只需要對 LINE 機器人傳送謠言的原文,LINE 機器人就會回傳由群眾協作的謠言真偽結果。 ### (子題一) 假新聞、媒體挑戰與社會機制 ### 1. 何謂假新聞?假新聞與謠言、錯誤資訊、劣質新聞等概念有何異同?假新聞的本質為何,應該如何定義? 假新聞、謠言、錯誤資訊與劣質新聞等,前三者可以很清楚的被視為假資訊的其中一員,裡面通常會有誤導、錯誤或移花接木的內容 。而劣質新聞則會出現較多訊息揭露不完全的問題,或許會造成誤導讀者的結果,但這類型多半是基於編輯流程不嚴謹,而產生的情況。 但可以確定的是,這些資訊都有可能造成社會對某事件的認知差異,而對於真相的誤解,甚至可能造成公共對話無法進行。這也是假新聞討論變得如此重要,且受重視的原因。 我覺得可以參考 First Draft 對於假新聞的分類,他主要分成 7 個類別: Satire or Parody - 諷刺、惡搞、反串 False Connection - 標題或圖片與內容不符 Misleading Content - 誤導、陷害之內容 False Context - 斷章取義、移花接木(上下文) Imposter Content - 仿冒來源 Manipulated Content - 偽造、惡意改作、移花接木(內容本身) Fabricated Content - 完全錯誤、意欲誤導或傷害之內容 接著他們又將假新聞會造成的影響,或是創造的意圖,再列出八種: poor journalism - 造成低品質新聞產出 to parody - 造成讀者模仿混淆 to provoke or to punk - 意圖挑釁與分化 passion - 意圖激化情緒 partisanship - 意圖強化黨派忠誠 profit - 意圖達成金錢利益 political influnence - 意圖創造政治影響力 propaganda - 意圖倡議某想法 如此一來就可以形成一個 2 維的矩陣,就可以大概地描繪出假新聞的類別與造成的影響。 以下是參考資料: https://firstdraftnews.com/fake-news-complicated/ First Draft formed as a nonprofit coalition with nine Founding Partners in June 2015 to raise awareness and address challenges relating to trust and truth in the digital age. ![FDN_7Types_Misinfo](https://firstdraftnews.com/wp-content/uploads/2017/02/FDN_7Types_Misinfo-01-1024x576.jpg) ![FDN_Misinfo_Matrix](https://firstdraftnews.com/wp-content/uploads/2017/02/FDN_Misinfo_Matrix-01-1024x576.jpg) ### 2. 當前的假新聞問題是否比過去更嚴重?主因是新舊媒體生態還是其他原因? 假新聞其實一直存在,過去與現在的差別只是訊息傳播速度的問題。造成的結果有沒有比過去嚴重,是比較難界定的。 假訊息傳播的管道,從以前塞滿住家的信箱的文宣品與報紙,電子信箱會收到轉發廣告郵件,後來用了 BBS 也有一堆轉文,接著就是現在的社群網路了。 假新聞的存在應該已經有好長一段的歷史,像19世紀的德國就存在假新聞為主的報紙,當時印刷技術的普及與紙張成本的下降,新聞機構紛紛成立,為了降低成本報導國外消息,許多[「fake foreign correspondent’s letter」](http://www.niemanlab.org/2017/04/how-the-techniques-of-19th-century-fake-news-tell-us-why-we-fall-for-it-today/?utm_source=Daily+Lab+email+list&utm_campaign=f4ec11ab73-dailylabemail3&utm_medium=email&utm_term=0_d68264fd5e-f4ec11ab73-396038557)誕生。 現在的傳播媒介變多、變得更便利,獲取資訊的方式也多樣化,所以得到的資訊量也提升了許多,假訊息量也跟著提升,造成的影響層面也更廣泛,但要實質評估影響性還是需要要有一個「量化的基準」,譬如說當年度因為假新聞而損失的財務之類的。但是這需要搜集更多假新聞相關的資訊,並採取社會科學的研究方法進行。 不可諱言的訊息傳播來源的增加,也會增強假訊息的影響力,觀察真的假的上面的轉傳連結,微信、QQ等網址的增加,是個值得繼續觀察的現象。 雖然假訊息的量增加,但同時求證的門檻也變低,這顯示科技進步的一體兩面。以前大家要自己求證(fact check)的門檻很高,沒有工具與管道,又費時。而現在普羅大眾只要熟悉一些工具就可以做到很有效的事實查證,使用 Google 搜尋、查詢 wikipedia、發文到社群媒體跟大家討論,都是可以嘗試的方法。 重點還是在於讀者個人對於資訊懷疑的認知培養,與查證的習慣是否養成。假訊息的傳播主力永遠是人,如果不從根源培養識讀能力,各方怎麼努力,恐怕還是無法降低假訊息的量。 ### 3. 假新聞對於社群媒體與傳統新聞媒體帶來了那些影響?假新聞與媒體公信力、媒體的角色、以及媒體的營運與發展帶來了那些挑戰? 我覺得跟「食安問題」造成的結果很像。大家不會因為有食安問題而拒絕進食,而是會影響大家消費的方向,譬如前一陣子的戴奧辛雞蛋、塑化劑等食安消息出現,會影響購買意願,而少買有問題的產品。 若假新聞對媒體的公信力、角色造成影響,某種程度來說也是淘汰了一些不適任的媒體。我們可以看到部分電子媒體在發新聞的時候,品質相對的低落,沒有作者署名、沒有內容相關來源、誇大的標題,其實都是很扣分的一個表現。 但是「劣幣驅逐良幣」的效應確實可見。點擊換取廣告流量為營運模式的時代,幾間內容品質低劣的農場媒體,他們的文章在廣大的社群平台使用者間轉傳,一定程度影響到整個大環境,營運的壓力回頭影響認真產製新聞的媒體,壓縮了記者的發揮空間。 傳統媒體除了端出更好的作品之外,也需要與讀者對話。好作品提供讀者另外一種閱讀視野,而對話則能理解螢幕另外一端的他們,內心的疑惑、渴求、想法是什麼,才能串連起「為何分享假新聞」的真實動機。 憑心而論,過往的新聞機構在面對社群時代,理解、溝通與對話的能力還未臻純熟。在埋頭產製新聞、控管報導品質的同時,也需要抬頭,多運用社群平台、串流影音平台作為好的輔助工具,與讀者對話,譬如澄清事實、發布消息、收集使用者回饋,進而提昇品質。 ### 4. 因應假新聞現象,社群媒體與傳統新聞媒體有何應採或可採的行動?目前各主要社群平台或傳統新聞媒體的因應措施有實質意義嗎?有其他可行的作法嗎? 這邊可以分享一些社群平台的作為, * https://www.blog.google/topics/journalism-news/expanding-fact-checking-google/ * https://www.nytimes.com/2016/12/15/technology/facebook-fact-checking-fake-news.html 這些是兩大資訊集中平台對抗假新聞的作為。而近期維基百科創辦人也打造一個新的平台[「Wikitribune」](https://www.wikitribune.com),目的在打擊假新聞,記者需與社群協作,而呈現形式最大的特點在於,記者需要提供事實來源,或者提供完整的訪談記錄和文字稿。 所有資訊免費提供給大眾閱讀,大眾用戶能修改與更改文章,但是更改須經適格人士批准才會生效。 「公開透明」是他們對抗假新聞的方法。 ### 5. 政府或其他社會團體對於假新聞現象,有何應採或可採的行動?例如,是否應該建立事實查核制度、新聞澄清措施、修訂相關法規、或執行獎懲措施?這些作法是否抵觸民主社會常規?是否影響新聞自由? 我其實不贊成政府直接建立事實查核制度,但是針對行政部門相關的謠言、假資訊,有系統的做事實查證,是有必要的。 政府內部有一些輿情觀察的機制,他們可以直接假新聞大宗轉傳平台搜集流言,依照實際的發生數據、實際轉傳的內文,回去製作官方的回應,譬如警政署、衛福部都有相關的闢謠專區,並且依照個別訊息被回報的數量,來排定回應的優先順序。 讓這些闢謠專區更活絡,且將這些闢謠的結果用開放資料且格式化的方式釋出。那麼可以讓第三方查證單如,譬如我們「真的假的」專案,或是「新聞小幫手」專案,可以利用這些資料來做闢謠。 透過第三方查證平台釋出的資訊,也能降低讀者因為資訊刊載平台立場不同的問題,而拒絕接受資訊的機會,增加闢謠成果的擴散。 接著有足夠的第三方查證平台提供這些實際的使用資訊,再回頭分享回饋給政府,讓政策溝通更加的 driven by data,這樣就有機會形成了一個正循環。像是內政部警政署 165 闢謠專區資料已經放上去了,不過專區資料並沒有做好的格式化,且將問與答分開,還比較難去利用。那如果有更多政府單位也支援的話,那大家就能用程式將他與資料庫做某種程度的整合,會更有效率。 參考資料: http://pending.schema.org/ClaimReview http://data.gov.tw/ ## 筆記 ggm 的亂記筆記 https://hackmd.io/s/B10hrH3eW

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