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美國2017NFL年國歌抗議活動Twitter視覺內容研究

為了解決Sina soccer的問題，作者林蕾娜 這樣論述：

Past research has delved into collective actions and political activism. However, few studies analyze the communication patterns of visual content generated on social media throughout protest events in relation to polarized political issues. Given the impacts of Twitter mobilizers to shape online o

pinions of protesters, this content analysis study examined Twitter image tweets during the 2017 National Football League (NFL) national anthem protests (NAP) in the United States. In order to understand visual communication trends on Twitter during the protest, this study will utilize the social id

entity theory (Tajfel and Turner, 1979) and Van Zomeren et al.’s (2012) dual pathway model (DDPM). The theoretical framework of integrating SIT and DDPM will facilitate the understanding of how social identity, grievances, and coping approaches (emotional route: affective responses; instrumental rou

te: efficacy) factor into social media image content during a polarized protest.This study analyzed tweeted images shared when the national anthem protests peaked in web search penetration. It collected image tweets with the Twitter Advanced Search function and used top tweet filter under the #TakeA

Knee hashtag. This research in total analyzed 1,400 viral NAP tweet images from September 24, to October 21, 2017. Based on research questions and past studies, xx codes have developed with xx emerging codes from the data.The visual content analysis showed that NAP supporters’ posts were primarily r

elated to the social identity of minority support; however, the visual content for constructive patriotism and sports fandom was minimal. Images depicting police brutality were scarce, possibly due to the peaceful nature of the NAPs. In terms of grievances, images regarding anti-protest mobilizers s

uch as Donald Trump were more prominent in the dataset. Consistent with past research, reciprocal affective responses (i.e. sympathy and admiration) were expressed more in the image tweets than shared affective responses (i.e. anger and sarcasm). Lastly, in regards to temporal changes for key codes,

the themes of majority of image tweets were not influenced by offline events or news outlets’ agenda setting over time. This indicates that the online communication patterns of NAP supporters do not always repeat or continue the topics in various news outlets and offline events. As the dataset did

not follow an identifiable pattern across the codes and subcodes in regards to temporal changes, common trends instead included image tweet spikes and volume alignment between codes on days that corresponded with offline events.

社群網絡分析在網路上輿情言論之應用

為了解決Sina soccer的問題，作者宋銘皓 這樣論述：

隨著網路的發達，社群媒體已成為日常生活中不可或缺的平台之一。過去民眾所關注的事件皆為新聞或其他報章雜誌所發現，現今許多事件或是新聞都從社群媒體中所挖掘，因此密切關注社群媒體內容已成為現今多數記者產生新聞的來源之一。本研究試圖觀察民眾在社群媒體上的評論行為，再進一步透過社群網絡分析以及情緒分析，以探索網路評論行為形成新聞事件之要素，同時藉由演算法及深度學習試圖從社群媒體評論行為預測是否轉變成為新聞事件。本研究針對中國時報、自由時報、聯合新聞及蘋果日報為研究對象，蒐集其引述來源為批踢踢實業坊以及Dcard，再追朔回其討論版上透過爬蟲程式蒐集資料。實驗結果顯示，隨機森林能夠有效預測是否成為新聞，並

找到其重要特徵，再藉由視覺化呈現觀察社群的討論版以及相關議題與各新聞媒體之間的關係。