The Impact of Artificial Intelligence on Social Media

Social media attributes to a conversational, disseminated form of content generation, distribution, and interaction amongst communities in a broad sense. Unlike broadcast-based traditional and industrial media, social media has cut down the barriers between readership and authorship. The consumption of information and method of dissemination is becoming intertwined with producing and sharing information.

Significant social media elements, such as user-generated content or consumer-generated media, have been viewed as the defining features of Web 2.0. From a tool perspective, a collection of Web-based applications represent the way social media functions. Examples include web blogs, microblogs, online forums, wikis, podcasts, live streams, social bookmarks, Web communities, social networking, and avatar-based virtual reality.

Social media has become a sole information source for many individuals to deal with knowledge and cognitive overload obstacles, get answers to particular questions, and find more relevant social and economic exchange possibilities. Moreover, it has become a platform for them to network. It gives all kinds of dynamic dialogues by sharing their expertise and opinions. It is safe to claim that social media has already infiltrated an array of applications with exceptional influence. Given the continued interest and the ever-growing information and meta-information generated through social media, will continue allowing new exciting applications whilst also revolutionizing many existing ones.

Social media analytics is about progressing and assessing informatics instruments and structures to compile, monitor, examine, review, and visualize social media data, usually driven by specific requirements from a target application. Social media analytics research has several purposes, such as expediting conversations and interaction between online communities while also obtaining useful patterns and intelligence to help entities that include active contributors in continuous dialogues.

Social media intelligence attempts to define actionable knowledge from social media in context-rich application frameworks, develop corresponding decision-making or decision-aiding structures, render architectural designs and solution frameworks for existing and new applications.

It presents great potential with significant practical relevance as a valuable, new area of analysis, potentially drawing on disciplines from Artificial Intelligence and other fields. Several fundamental challenges currently face social media intelligence research. First, social media intelligence research calls for highly integrated multidisciplinary research.

Social media content takes the form of streams — dynamically arriving and updated series of texts with structured and unstructured components.

Social media analytics research shows clear signs of outgrowing its reference disciplines, such as text mining and Web computing, with its unique problem characteristics and solution frameworks. Social media intelligence research is also growing with an expanding set of applications and exciting mixing with other academic disciplines.

References

D. E. O’Leary, “Artificial Intelligence and Big Data,” in IEEE Intelligent Systems, vol. 28, no. 2, pp. 96–99, March-April 2013, doi: 10.1109/MIS.2013.39.

Machine learning explained: How Facebook uses it to learn more about you. (2019, February 12). [Video]. YouTube. https://www.youtube.com/watch?v=mSV1X-Q6MQY

You Will Wish You Watched This Before You Started Using Social Media | The Twisted Truth. (2018, April 20). [Video]. YouTube. https://www.youtube.com/watch?v=PmEDAzqswh8

Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social Media Analytics and Intelligence. IEEE Intelligent Systems, 25(6), 13–16. https://doi.org/10.1109/mis.2010.151

This blog is a project for Study Unit MCS5460, University of Malta.