Echobox Publishing
Echobox is a tech startup that assists traditional publishers to curate their content on social media automatically.[1] It was founded in 2013 by Antoine Amann,[2] and headquartered in London.[3]
Echobox collects referral clicks from social feeds and uses topic clustering and event detection to compare interest in certain news articles on a segment of the audience’s social feeds. They also do a form of a/b split testing.[4]
By 2022, Echobox claims more than 100 employees and a thousand customers in around a hundred countries [5] and a publicly visible uptime status report.[6]
News media critics have focused on clickbait headlines[7] or content analysis. While ignoring the architectures that are built to stage or share news among partisan enclaves, building up a loyal audience or unpacking reader perceptions. Urban elites have identified certain people who can deliver niche content as a mechanism a for gaining self-advancement and gaining patronage.
References
- ↑ O'Hear, Steve (2016-07-04). "Echobox raises $3.4M to let publishers intelligently share content to Twitter and Facebook". TechCrunch. Retrieved 2025-04-15.
- ↑ "Antoine Amann". STM Association. Retrieved 2025-04-15.
- ↑ "Artificial Intelligence is coming for publishers' analytics | What's New in Publishing | Digital Publishing News". What’s New in Publishing | Digital Publishing News. 2017-10-27. Archived from the original on 2023-06-15. Retrieved 2025-04-15. Unknown parameter
|url-status=ignored (help) - ↑ Kahn, Jeremy (28 October 2016). "It Took Robots for This French Newspaper to Conquer Twitter".
- ↑ "Echobox: Boost your referral traffic and engagement". www.echobox.com. Retrieved 2025-04-15.
- ↑ "Echobox Status". status.echobox.com. Retrieved 2025-04-15.
- ↑ Al-Sarem, Mohammed; Saeed, Faisal; Al-Mekhlafi, Zeyad Ghaleb; Mohammed, Badiea Abdulkarem; Hadwan, Mohammed; Al-Hadhrami, Tawfik; Alshammari, Mohammad T.; Alreshidi, Abdulrahman; Alshammari, Talal Sarheed (2021-10-13). "An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks". Applied Sciences. 11 (20): 9487. doi:10.3390/app11209487. ISSN 2076-3417.
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