{
  "id": 16754420,
  "title": "Crowdsourced collaborations: Using AI, social media to find potential brand partners",
  "url": "https://urgent.news/2026/10/27/crowdsourced-collaborations-using-ai-social-media-to-find-potential",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-27T14:00:10.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-10-crowdsourced-collaborations-ai-social-media.html"
  },
  "original_language": "en",
  "account": "In a recent study published in Information Systems Research, Penn State researchers have developed an AI-powered framework to help businesses identify potential brand collaborations. Traditionally, brand managers rely on time-consuming surveys and screenings to find suitable matchups, but this new method leverages social media chatter and consumer sentiment to uncover promising opportunities.\n\nUsing a combination of traditional brand characteristics and social media data, the framework provides a curated list of brand pairs tailored to specific businesses. In a survey of brand managers, the model had nearly a 90% chance of selecting favorable brand pairs, compared to eliminating those deemed unlikely.\n\nThe framework operates through a four-step system, beginning with signal fusion, which synthesizes various pieces of information into cohesive insights. The first phase combines brand data with co-mentions on social media to create a comprehensive network of brand connections. The researchers analyzed 450 top business-to-consumer brands and 477,933 Twitter posts mentioning these brands together in 2020. They filtered out complaint-related posts and used AI-based text classifiers to determine whether consumers viewed brands as complementary or competing.\n\nThe researchers then combined these insights with brand characteristics and co-mention frequencies to develop a broader network of brand relationships. The framework also included phase two, which surveyed stakeholders about the model's proposed brand alliances. In this phase, 57 brand managers evaluated 24 brand pairs and selected up to eight promising pairs, five times more often than the eliminated pairs.\n\nPhase three of the framework categorizes proposed collaborations based on factors such as frequency of mention in posts, sentiment, and engagement metrics. The model then plots the proposals on a matrix with four quadrants: premier opportunities, high-potential alliances, hidden niches, and less promising prospects. This stage provides a more systematic view of the opportunities, helping stakeholders prioritize collaborations.\n\nFinally, phase four uses natural language processing and generative AI to generate practical guidance for individual brand collaborations based on insights from social media posts. The generated guidance details which specific marketing elements are dominating consumer discourse, allowing businesses to make informed decisions about potential brand alliances.",
  "summary": "Brand collaborations have been growing in popularity as a powerful marketing tool for businesses looking to generate hype and expand their customer base, but brand managers often rely on costly and time-consuming surveys and screenings to identify potential matchups. A recent study by a Penn State researcher and collaborators proposes a new method: an artificial intelligence (AI)-powered…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}