Media intelligence
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Media intelligence uses data mining and data science to analyze public, social and editorial media content. It refers to marketing systems that synthesize billions of online conversations into relevant information. This allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy.
Media intelligence can include software as a service using big data terminology.[1] This includes questions about messaging efficiency, share of voice, audience geographical distribution, message amplification, influencer strategy, journalist outreach, creative resonance, and competitor performance in all these areas.
Media intelligence differs from business intelligence in that it uses and analyzes data outside company firewalls. Examples of that data are user-generated content on social media sites, blogs, comment fields, and wikis etc. It may also include other public data sources like press releases, news, blogs, legal filings, reviews and job postings.
Media intelligence may also include competitive intelligence, wherein information that is gathered from publicly available sources such as social media, press releases, and news announcements are used to better understand the strategies and tactics being deployed by competing businesses.[2]
Media intelligence is enhanced by means of emerging technologies like ambient intelligence, machine learning, semantic tagging, natural language processing, sentiment analysis and machine translation.
Technologies used
[edit]Different media intelligence platforms use various technologies for monitoring and curating content, engaging with audiences, analyzing data, and measuring communications and marketing success. Providers may obtain content by scraping websites, using social media or other platform APIs for third-party developers, or purchasing data from resellers.
Some social media monitoring and analytics companies send queries to data providers each time a user submits a search. Others archive and index social media posts to offer on-demand access to historical data, allowing methodologies and technologies that leverage network and relational information. Some companies also use crawlers and spidering techniques to identify keyword references, often applying semantic analysis or natural language processing. In general, these tools gather social media data at scale and process it to produce meaningful insights.[3][4]
See also
[edit]- Ambient awareness
- Content intelligence
- Creator economy
- Cultural technology
- Influence-for-hire
- Marketing and artificial intelligence
- Marketing intelligence
- Media monitoring
- Social bot
- Social cloud computing
- Social marketing intelligence
- Social media intelligence
- Social media monitoring
- Social software
- Virtual collective consciousness
References
[edit]- ^ Leslie Nuccio (January 19, 2015). "Digital Breadcrumbs and the New Media Intelligence". Social Media Today. Retrieved March 23, 2017.
- ^ Oh, Onook; Agrawal, Manish; Rao, H. Raghav (2013). "Community Intelligence and Social Media Services: A Rumor Theoretic Analysis of Tweets During Social Crises". MIS Quarterly. 37 (2): 407–426. doi:10.25300/MISQ/2013/37.2.05. ISSN 0276-7783. JSTOR 43825916. S2CID 16343216.
- ^ De, Shaunak; Maity, Abhishek; Goel, Vritti; Shitole, Sanjay; Bhattacharya, Avik (2017). "Predicting the popularity of instagram posts for a lifestyle magazine using deep learning". 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). pp. 174–177. doi:10.1109/CSCITA.2017.8066548. ISBN 978-1-5090-4381-1. S2CID 35350962.
- ^ "What Is Social Media Analytics?". IBM MSpaces. August 6, 2021. Archived from the original on May 20, 2023. Retrieved June 23, 2025.