The Selfish Web: Is Sharing Really Caring?

In an earlier post, we described how the term “social search” can be roughly translated to mean “searching the social Web” for many of the offerings out there. We argued that social search can be thought of as a process, not just another source of results. Also, the differing intents behind sharing on social networks and searching may result in lower relevance when using social posts as search result recommendations. Here we will discuss how the social Web – although undoubtedly a useful source of relevant results for searching – may not cover the gamut of peoples’ searching interests and that the motivations behind posts on social media sites may limit their usefulness as a reliable source of search recommendations.

Sharing is only partly caring

The psychology of sharing has been well studied in recent times, with many motivations identified as to why people choose to post things to social media. Altruism, self-promotion, validation, relationship-building; all feature across various studies, as does building thought leadership or authority, image- or brand-construction. Even altruism itself can be viewed as self-serving, though studies have shown that humans may possess an “altruism instinct“.


Public vs Private Parts: Personas & Sharing on the Recommendation Web

The Internet is an archive of our lives. Every event captured, every photo rendered, and every conversation indexed. And, if it can be stored it can be found. Maybe not today but someday, by a friend perhaps, or a future employer, whether we want it to be found or not. This is both liberating and terrifying. But is it creating an incentive for people to hide their true personalities? Are we curating carefully crafted personas online that only disguise our genuine personalities? If so then, what we share may not be what we click and this has some important implications for the future of personalization, sharing, and recommendation on the web.

Figure 1.  An analysis of what people share versus what is clicked, by 33Across, and based on 450 large publishers and 24 content categories.

Figure 1. An analysis of what people share versus what is clicked, by 33Across, and based on 450 large publishers and 24 content categories.


Whitepaper – Social Search and Search Analytics in the Discovery Economy: An Enterprise Perspective

By Prof. Barry Smyth, HeyStaks' Chief Scientist

By Prof. Barry Smyth, HeyStaks’ Chief Scientist

Knowledge workers continue to struggle when it comes to finding the right information at the right time, leading to high search failure and abandonment rates – 50% of queries lead to failed searches and 44% of knowledge-workers fail to find what they are looking for – a significant cost to enterprise in terms of lost productivity and missed opportunities. One practical solution is for a more collaborative approach to search, which harnesses the past search patterns of experts within an organisation, and works in tandem with conventional search services to provide more relevant and useful results. By harnessing the power of collaborative search, HeyStaks can improve search effectiveness within the enterprise by up to 50% and by fostering improved collaboration HeyStaks will improve engagement, knowledge sharing, and innovation right across an enterprise.