在社群媒體平台上，爭議性議題經常導致極端化的互動，使媒體使用者明確地分化為支持和反對兩個陣營。根據「沉默螺旋」理論，當人們感知自己的觀點與周圍環境不同時，他們傾向保持沉默。這種現象容易使某一特定立場的群體主導輿論。然而，先前的研究對此互動和意見主導之間的潛在機制尚無確切之定論。為了深入理解這些機制，我們收集了 Reddit 上針對議題的評論，並標示出這些留言互動網路中的不同類型使用者，包括支持者、反對者和中立者。接著，我們運用指數隨機圖模型（ERGM）和時間指數隨機圖模型（TERGM）進行分析，以研究影響爭議性議題中意見主導的關鍵互動網絡結構。
我們的研究發現，不管主流觀點為何，反對者往往會向持有不同觀點的人表達自己的看法。此外，不論是爭議性還是非爭議性的議題，使用者都更傾向於與持相同觀點的人互動。而隨著時間推移，這種互相回應的互動模式會促使使用者持續參與討論，但回應持有不同觀點的人的異質性互動無法吸引更多的參與者並推動網路成長。此外，我們也注意到樣本規模的大小可能會限制 ERGM 和 TERGM 網路結構分析結果的統計顯著性。本研究期待為線上社群媒體的動態網絡分析提供新的洞察，也希望能夠提供實用的操作建議，有助於減少社交媒體平台上的沉默螺旋和同溫層效應，並為數位行銷人員在理解消費者行為方面開提供新的切入點。

Controversial issues on social media often result in polarized interactions, dividing users into opposing groups. The "spiral of silence" theory suggests that individuals tend to remain silent when surrounded by differing viewpoints. This dynamic allows a specific faction to dominate public opinion. However, previous studies lack conclusive evidence on the underlying mechanisms of user interaction and opinion dominance. To address this gap, we collected Reddit comments on controversial topics and conducted a longitudinal analysis using the Exponential Random Graph Model (ERGM) and Temporal Exponential Random Graph Model (TERGM).
Our study found that regardless of the majority opinions, opponents tend to express their views to those with different viewpoints. In addition, users tend to interact more with like-minded individuals, regardless of whether the issue is controversial or uncontroversial. Such reciprocal engagement keeps users active in discussions, while diverse interactions do not notably foster network growth. We also discovered that a small sample size may affect the statistical significance of ERGM and TERGM results. We hope to offer critical insights into the dynamics of online social networks, with potential applications in reducing the spiral of silence and echo chamber effects, thereby providing valuable perspectives for digital marketers to enhance their understanding of contemporary consumer behavior.

Abstract 2
摘要 3
List of Contents 4
1. Introduction 6
1.1 Research Background 6
1.2 Research Motivation 7
1.3 Research Question 8
2. Literature Review 9
2.1 Spiral of Silence Theory and Dynamics of Online Interactions 9
2.2 Controversy and Polarization in Social Media 10
2.3 The Influence of Internet Memes 12
3. Hypothesis 13
4. Methodology 16
4.1 Data Collection 17
4.2 Users’ Stance Identification 20
4.3 Network Formation 22
4.4 Social Network Analysis (SNA) with ERGM 23
Exponential Random Graph Model (ERGM) 23
Temporal Exponential Random Graph Model (TERGM) 24
5. Data Analysis 26
5.1 Network Structure Characteristic 26
Comparison of Controversial and Uncontroversial Memes 26
Network graph with Dynamic Process of Representative Memes 29
5.2 Analysis Results of ERGM on Network Characteristics 34
Relational Effects: Heterophily and Homophily 34
Structural Effects: Transitivity 37
5.3 Analysis Results of TERGM on Network Dynamics 38
Relational Effects: Heterophily and Homophily 38
Structural Effects: Transitivity 40
5.4 Phase 2: ERGM and TERGM with a Larger Network 41
Larger Network: Amalysis Results of ERGM 42
Larger Network: Amalysis Results of TERGM 44
6. Discussion 46
6.1 Findings 46
Comparison of Controversial and Uncontroversial Memes 46
Relational Network Effects: Heterophily 47
Relational Network Effects: Homophily 47
Structural Network Effects: Transitivity 48
6.2 Conclusion 49
6.3 Managerial Implications 50
6.4 Limitation 51
6.5 Future Work 52
7. Reference 54
8. Appendix 59

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