This study examines how X influences consumer behavior through real-time
interactions, peer recommendations, and algorithm-driven content exposure.
It explores how engagement metrics, influencer credibility, and social proof shape
purchasing decisions. This research is guided by three hypotheses: that higher
engagement metrics increase consumer trust in brand messages, that peer
recommendation and influencer endorsements have a stronger impact on consumer
purchasing decisions than direct brand advertisements, and that algorithmic-driven
content exposure reinforces pre-existing consumer biases, limiting decision-making
autonomy.
The research employs a qualitative approach, using netnography and discourse
analysis supported by Python-based tools for sentiment and topic modeling to analyze
consumer discussions on X. Findings reveal that consumer trust depends on
transparency, interactive brand engagement, and authentic peer reviews.
Influencer-driven content and algorithmic filtering significantly impact
decision-making, often reinforcing pre-existing biases.
The study highlights the ethical implications of targeted marketing, synthetic
engagement, and data privacy concerns. It provides insights for businesses on balancing
marketing effectiveness with consumer trust. Policymakers must address algorithmic
influence and misinformation to protect consumer autonomy. Future research should
examine cross-platform digital influence and the role of emerging technologies in
shaping consumer preferences.
This work is protected by copyright and/or neighboring rights. It can be freely used for personal use, scientific research, or self-education. Other uses require permission from the right holder(s).
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