輿情大數據之公關危機處理有效性分析－以蔡阿嘎及Toyz的熱點事件為例

一個好的公關要能迅速有效處理公關危機事件，過去在沒有網路大數據的時代，公關危機處理的判斷都是倚賴公關人員的經驗。隨著網路媒體的快速發展，資訊傳播的即時性和多元化，使得負面的輿情事件很容易引爆，甚至延燒，目前網路上已發展出輿情大數據系統，是否能幫助驗證公關危機處理的有效性？本研究利用輿情大數據工具，探索YouTuber的公關危機處理之有效性。研究發現輿情大數據工具OpView原提供之輿情指標比較無法反映輿情的明顯變化，因此本研究重建了一項觀測輿情轉變的新指標，並透過統計特徵說明其有效性。YouTuber蔡阿嘎雖然在短時間內做出兩次符合公關危機處理原則的回應，但是輿論情緒顯示沒有太大效果，本研究從實際的數據分析驗證Fearn-Banks的危機溝通理論，發現回應時間不是最重要的，而是展現出「誠意」的道歉貼文，才看到輿情顯著反轉。而網紅Toyz的負面議題操作從輿情數據可以觀察出每次都能製造出一定的聲量，在出現負面輿情時，Toyz以直播及後製影片等方式，製造娛樂效果，讓輿情在短時間內轉為正向，可謂是自媒體新的公關操作方式，但仍應注意小報化的現象。本研究觀察到兩位YouTuber在負面議題都創造極大聲量，但導回正向輿情的方式不盡相同，蔡阿嘎是採取較傳統的公關危機處理方式，而Toyz是以負面行銷的方式，採綜藝娛樂化的處理，引發觀眾從娛樂效果對其提升好感度。研究也發現公關危機的輿情轉變，是從負面趨於「中立」再轉為正面聲量，從負面先漸漸出現中立的留言，而不是一般想像中，直接從負面轉為正面的輿情。且兩位研究對象皆在公關危機道歉後，透過後續影片極大的娛樂效果，使得負面輿情皆在經過約20天，歸一性正負情緒比轉為正向。從研究可以發現負面行銷的聲量，雖然大量但較為突發，且維持也不長久，實屬難以掌控，建議YouTuber等議題設定人士，在構思及面對時，可參考輿情大數據的即時聲量與情緒轉變，尤其是當事件發生的開始，發現輿情風向的第一時間，亦可搭配公關書籍的處理建議，作出妥適應對，避免後續引爆輿論一發不可收拾，越演越烈的棘手狀況。本研究之方式與結果可供自媒體主、企業品牌、政治人物等公關經營者參考。

關鍵字

大數據分析；公關危機管理；網路輿情；熱點關鍵字； YouTube經營

並列摘要

A good public relations practitioner must effectively manage crises promptly. Previously, in the pre-internet big data era, PR crisis management relied heavily on the experience of PR personnel. However, with the rapid development of online media and the immediate and diverse nature of information dissemination, negative public sentiments can easily explode and even escalate. Presently, sentiment big data systems have been developed online, but can they validate the effectiveness of PR crisis management? This study employs sentiment big data tools to explore the effectiveness of crisis management by YouTubers. The findings indicate that the sentiment indicators originally provided by the sentiment analysis tool, OpView, were unable to reflect significant changes in public sentiment. Therefore, this research developed a new index to monitor shifts in sentiment and used statistical features to demonstrate its effectiveness. Despite YouTuber Tsai A-Ga making two responses in line with PR crisis management principles within a short timeframe, public sentiment showed little effect. This research validated Fearn-Banks' crisis communication theory through actual data analysis, discovering that the timing of the response is not as crucial as demonstrating "sincerity" in apology posts, which then led to a significant reversal in sentiment. Internet celebrity Toyz managed to consistently generate substantial buzz with each negative issue through live streams and post-produced videos for entertainment, quickly shifting sentiments positively. This represents a new PR tactic in self-media, though the potential trivialization of content should be noted. The study observed that both YouTubers generated significant buzz on negative topics, but their methods of redirecting sentiments positively differed. Tsai A-Ga adopted a more traditional PR crisis management approach, whereas Toyz utilized negative marketing with an entertainment-focused strategy, enhancing audience favorability through the entertainment value. The research also noted that the sentiment transition during a PR crisis moved from negative to "neutral" before turning positive. Neutral comments gradually appeared amidst the negativity, rather than an immediate switch from negative to positive sentiment as commonly perceived. Both subjects, after apologizing for the crisis, used subsequent videos with high entertainment value to turn the sentiment positive after approximately 20 days. The study suggests that although the volume of negative marketing is substantial and sudden, it is also short-lived and challenging to control. It recommends that YouTubers and other content creators refer to real-time sentiment and emotional transitions provided by sentiment big data, particularly at the onset of an incident. Combining this with PR literature recommendations can help devise appropriate responses to prevent uncontrollable escalation of public sentiment. The methods and findings of this study may serve as a reference for self-media personalities, corporate brands, and political figures in their PR management strategies.