宗教與多元性別議題之網路輿情觀察－觀測時程：2013年9月至2013年12月

Observation of Internet Public Opinions on Issues about Religion and Gender Pluralism-Observation Period: September 2013 to December 2013

楊喨智(Liang-chih Yang)

《臺灣宗教研究》 14卷2期 (2015/12) Pp. 53-63

摘要

本研究將大數據（Big Data）之社群分析概念，運用於宗教文化與多元性別領域，進行網路聲量與正負情緒比之網路輿情觀察。首先以同性戀與宗教之關鍵字為主要研究主題，進行臺灣地區網路社群大數據之網路發文分析，並以2013年9月至2013年12月特定事件為觀測時程，觀測在特定事件之前後，網路發文之正負面情緒變化與正負面文章數比值。之所以選在這個時段作為觀測對象，原因是：10月25日當天，立法院婚姻平權草案一讀通過，10月26日，同運人士舉行了同志大遊行，至11月30日，護家盟舉行了宗教團體大遊行。因此這個時段正巧可以作為特定事件發生前與特定事件發生後的對比觀測。研究結果發現，當文章中有提到「同性戀」一詞時，若在整篇文章其他地方同時出現宗教相關字詞，以電腦智能語意分析判別內容，發現其情緒百分比，不論是正面的、負面的，皆有較多的情緒呈現；三觀測階段中，「同性戀」加上「宗教」相關字詞後的網路聲量增幅與負面情緒比值，也比「同性戀」一詞的網路聲量增幅與負面情緒比值，呈現大幅增加的趨勢。表示當「宗教」相關字詞出現在描述「同性戀」的網路發文中，其屬負面評述的文章比單一觀測「同性戀」一詞時比重較多，帶情緒的發文情況也較高。

關鍵字

大數據 ； 多元性別 ； 同性戀與宗教 ； 正負情緒比

並列摘要

This research takes the social group analysis concept of big data and employs it in the field of religious culture and gender pluralism to conduct a survey on public internet sentiments based on the volume of internet posts and their positive/negative emotion ratio. First, using gay/lesbian and religion keywords as the primary research subject, an analysis of internet posts based on big data of Taiwanese internet groups was conducted, with events from September 2013 to December 2013 designated as the observation timeframe. The changes in positive/negative emotions of internet posts and the numerical ratio of positive/negative articles before and after the designated events were observed. The reason why this period was chosen as the target of observation was for a number of reasons: on October 25, 2013, a first reading of the Marriage Equality Bill (hunyin pingquan cao'an 婚姻平權草案) passed in the Legislative Yuan; on October 26, a Taiwanese LGBT pride parade was held; and, on November 30, the Alliance for Protection of the Family (護家盟) held a large religious organization parade. Therefore, this time period is opportune for comparative surveys of before and after these designated events. From the research results, we discover that when an article referred to words dealing with gay/lesbian, if in other parts of the whole article there occurred religion-related words at the same time, more emotional sentiments emerge regardless of whether the article is positive or negative. This was distinguished through a computer intelligence semantics analysis. During three observation phases, the number of internet posts with gay/lesbian terms after religion related words were added increased along with the ratio of negative emotions, compared to that of the appearance of only gay/lesbian terms, and its ratio of negative emotions. The numerical values presented a substantial trending increase. This indicates that when religion related words appear in internet posts commenting on gay/lesbian lifestyles, the percentage of those articles that are classified as negative commentary is greater than when gay/ lesbian words are observed by themselves. The conditions of posts that bear emotions are also greater.

並列關鍵字

big data ； gender pluralism ； gay/lesbian and religion ； positive/negative emotion ratio

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