Algoritmik Nostalji: Makine Öğrenimin Geçmişle Duygusal Bağlantılarımızı Şekillendirme Yöntemleri

Abstract

Algoritmik olarak şekillenen medya ortamlarının giderek etkisini artırdığı günümüzde, nostalji dijital platformlar içinde güçlü bir duygusal araç hâline gelmiştir. Bu çalışma, Instagram ve Twitter’daki algoritmik öneri sistemlerinin nostalji temalı içeriklerin oluşumunu nasıl kolaylaştırdığını ve kullanıcıların duygusal katılımını nasıl etkilediğini incelemektedir. Marka hesapları tarafından paylaşılan geçmişe dönük içeriklerin, kolektif belleği harekete geçirecek biçimde nasıl kurgulandığı ve algoritmalar aracılığıyla görünürlük kazandığı analiz edilmiştir. Çalışma, nitel dijital etnografi yöntemiyle yürütülmüş; Instagram ve Twitter’da seçilen halka açık marka hesapları üzerinde müdahalesiz gözlem yapılmıştır. Özellikle #TBT ve #ThrowbackThursday etiketleriyle paylaşılan içerikler incelenerek dijital nostaljinin algoritmalar yoluyla nasıl yeniden üretildiği değerlendirilmiştir. Bulgular, algoritmaların nostalji temalı içerikleri öne çıkararak kullanıcıların duygusal bağlarını ve topluluk hissini güçlendirdiğini, ancak aynı zamanda veri görünürlüğü ve kullanıcı özerkliği konularında endişeleri de artırdığını ortaya koymaktadır. Bazı kullanıcıların içeriklerle kurdukları etkileşimleri sonradan kaldırdıkları gözlemlenmiş, bu durum dijital etkileşimlerinin yarattığı görünürlük ya da sonuçlara dair bir rahatsızlık hissine işaret etmektedir. Sonuç olarak, dijital nostaljinin duygusal sürekliliği teşvik ettiği kadar, kişiselleştirme ve veri etiği meselelerini de pekiştirdiği görülmektedir. Bu çalışma, duygusal deneyimle algoritmik arabuluculuk arasındaki ilişkiyi ele alarak, dijital kültürde duygusal bağlılık ve veri mahremiyetine dair endişelerin birlikte nasıl şekillendiğine dair derinlemesine bir anlayış sunmayı amaçlamaktadır.

Keywords

• Algoritmik Öneriler
• Dijital Nostalji
• Makine Öğrenmesi
• Medya
• İletişim

Algorithmic Nostalgia: How Machine Learning Curates Our Emotional Connections to the Past

Abstract

In an era increasingly shaped by algorithmically curated media environments, nostalgia has become a powerful emotional tool within digital platforms. This study investigates how algorithmic recommendation systems on Instagram and Twitter facilitate the emergence of nostalgia-themed content and influence users' emotional engagement. The analysis explores how brand-generated throwback posts, strategically crafted to evoke collective memory and sentiment, intersect with user interaction patterns and algorithmic visibility mechanisms. Using a qualitative digital ethnography approach, this research is based on non-intrusive observation of selected public brand accounts on Instagram and Twitter. Content using hashtags such as #TBT and #ThrowbackThursday was examined to understand how digital nostalgia is amplified through algorithmic circulation. The findings reveal that algorithm-driven promotion of nostalgic content not only enhances users’ emotional connectivity and sense of community but also generates concerns about data visibility and user autonomy. Some users were observed to later remove their engagements, suggesting a potential discomfort with the visibility or implications of their digital interactions. The study highlights that digital nostalgia—while fostering emotional continuity—can also reinforce the complexities of personalization and data ethics in platform economies. By focusing on the interplay between emotional experience and algorithmic mediation, the study contributes to a deeper understanding of how affective engagement and concerns over data privacy co-evolve in contemporary digital culture.

Keywords

• Algorithmic Recommendations
• Digital Nostalgia
• Machine Learning
• Media
• Communicaiton

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