在社群媒體上流傳著大量的假新聞，事實查核者試圖驗證或反駁，並傳播經 過驗證的資訊以對抗假新聞。然而，對於如何有效促進事實查核內容在社交媒體上 傳播給線上用戶，我們的了解仍然有限。透過自我選擇偏誤和訊息情緒極性的角度 ，本研究探討了事實查核的各種因素，包括驗證評級和事實查核內容的情緒極性， 如何增加社群媒體上事實查核的傳播。

本研究首先進行了次級資料研究，使用 2008 年 5 月至 2022 年 9 月期間來自社 交媒體中，官方事實查核的資料集，共計 14,645 條記錄，並使用負二項回歸對資料 集進行分析。隨後，透過邀請 728 名社群媒體線上用戶再進行實驗研究，並使用 ANCOVA 分析數據，進一步確認次級研究的結果。兩項子研究中，次級研究中事實 查核的傳播為衡量實際轉發次數，實驗研究則是衡量受測者的轉發意圖。

在兩項研究和一項穩健性測試中，我們的研究結果顯示，在社交媒體上具有 極端評級("True"、"Pants on Fire!"或"False")的比具有中等評級("Mostly True"、 "Mostly False"或"Half True")的事實查核獲得更多的轉發次數和轉發意圖。具體而言 ，在極端評分中被評為"True"的推文的轉發次數和轉發意圖最高。作者們觀察到，事 實查核內容中的正面情緒比中性或負面情緒更有可能增加轉發次數和轉發意圖。

可是次級資料顯示了社交媒體上事實查核內容的模式不一致。當社群媒體上 的官方事實查核者將內容評為負面時，更有可能在事實查核內容中使用負面情緒， 例如"False"(59.5%)。相對地，當內容被評為正面，如"True"(9.8%)時，他們展 現出較低的傾向去包含正面情感。反而，當被評為正面時，他們主要使用中性情感 在事實查核內容中，例如"True"(70.7%)。作者們建議，當事實查核內容的情緒極 性和驗證評級一致時(例如，正面情感和正面評級，或負面情感和負面評級)，與 不一致時(例如，正面情感和負面評級，或負面情感和正面評級)相比，可以增加 轉發數量和轉發意圖，因為感知一致性中介了這種相互作用效應。

這些研究發現為官方事實查核者提供了寶貴的見解和網路使用者，藉由設計 和運用基於上述兩個關鍵因素的事實查核的有效策略:即驗證評級和事實查核內容 的情緒極性，以提高社交媒體上資訊的準確性。

While a surge of fake news circulating on social media, fact-checkers attempt to verify or refute and spread the verified information to combat the fake news. However, there is a limited understanding of how to effectively promote the spread of fact-checking contents on social media. Even fewer studies have investigated how to design sentiment polarity of fact-checking contents based on varying levels of verified ratings. Through the lens of self- selection bias and sentiment polarity of contents, this study investigates how factors of fact- checking contents, including verified ratings and sentiment polarity of contents, can increase the spread of fact-checking fact-checking posts/tweets across social media.

This paper conducted a secondary research using a dataset of 14,645 records of fact- checking tweets from the official fact-checker on social media between May 2008 and September 2022 and analyzed the dataset with negative binomial regression. Subsequently, the authors conducted an experimental research by inviting 728 online users on social media and analyzed the data with ANCOVA to further consolidate the findings of the secondary research. While the spread of fact-checking contents on social media in the secondary research was measured by a number of retweets, the intentions to retweet were measured to the experimental research.

Across two studies and one robustness test, our findings revealed that fact-checking tweets with an extreme rating ("True," "Pants on Fire!," or "False") on social media received a higher number of retweets and intentions to retweet than the tweets with a moderate rating ("Mostly True," "Mostly False," or "Half True"). Specifically, among the extreme ratings, tweets rated as "True" had the highest number of retweets and intentions to retweet. The authors observed that positive sentiment in fact-checking tweets was more likely to increase number of retweets and intentions to retweet than neutral or negative sentiment.

However, secondary data indicated an inconsistent pattern of fact-checking contents on social media. Official fact-checkers on social media were more likely to use negative sentiment in fact-checking contents when those contents were rated as negative, such as "False" (59.5%). Conversely, they exhibited a lower inclination to include a positive sentiment in fact-checking contents when the contents were rated as positive, such as "True" (9.8%). Instead, they predominantly used a neutral sentiment in fact-checking contents when it was rated as positive (e.g., "True": 70.7%). The authors suggested that the congruency effect between sentiment polarity of fact-checking contents and verified ratings (e.g., positive sentiment with positive ratings or negative sentiment with negative ratings) could increase number of retweets and intentions to retweet compared to the incongruency effect (e.g., negative sentiment with positive ratings or positive sentiment with negative ratings) because the sense of congruency mediated these two effects.

These findings provide valuable insights for official fact-checkers and online users to design and implement effective strategies of fact-checking posts/tweets based on two crucial factors: verified ratings and sentiment polarity of fact-checking contents, to promote the veracity of information on social media.

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