Confirmed to be in testing, a new
Facebook app feature called 'Add a Link' lets users see popular articles they might want to include in their status updates and comments by entering a search query. The results appear to comprise articles that have been well-shared by other Facebook users, with the most recently published given priority over others. The option certainly makes it easier for users to add links without manually searching their News Feed or resorting to a
Google query. This new app reduce users' reliance on
Google Search.
Twitter announced it is replacing its 'Discover' tab with 'Tailored Trends'. The new Tailored Trends feature, besides showing Twitter trends, will give a short description of each topic. Since trends tend to be abbreviations without context, a description will make it more clear what a trend is about. The new trends experience may also include how many Tweets have been sent and whether a topic is trending up or down. Google may be falling behind in terms of social search, but in reality they see the potential and importance of this technology with
Web 3.0 and
web semantics. The importance of social media lies within how
Semantic search works. Semantic search understands much more, including where you are, the time of day, your past history, and many other factors including social connections, and social signals. The first step in order to achieve this will be to teach algorithms to understand the relationship between things. However this is not possible unless social media sites decide to work with search engines, which is difficult since everyone would like to be the main toll bridge to the internet. As we continue on, and more articles are referred by social media sites, the main concern becomes what good is a search engine without the data of users. One development that seeks to redefine search is the combination of
distributed search with social search. The goal is a basic search service whose operation is controlled and maintained by the community itself. This would largely work like
Peer to Peer networks in which users provide the data they seems appropriate. Since the data used by search engines belongs to the user they should have absolute control over it. The infrastructure required for a search engine is already available in the form of thousands of idle desktops and extensive residential broadband access. Despite the advantages of
distributed search, it shares several same security concerns as the traditionally centralized case. The security concerns can be classified into three categories:
data privacy,
data integrity and secure social search. Data privacy protection is defined as the way users can fully control their data and manage its accessibility. The solutions for data privacy include information substitution, attributed based
encryption and identity based broadcast encryption. The data integrity is defined as the protection of data from unauthorized or improper modifications and deletions. The solutions for data integrity are
digital signature, hash chaining and embedded signing key. The solutions for secure social search are
blind signature,
zero knowledge proof and resource handler. Another issue related to both distributed and centralized search is how to more accurately understand
user intent from observed multimedia data. The solutions are based on how to effectively and efficiently leverage social media and search engine. A potential method is to derive a user-image
interest graph from social media, and then re-rank image search results by integrating social relevance from the user-image interest graph and visual relevance from general search engines. Besides above engineering explorations, a more fundamental and potential method is to develop social search systems based on the understanding of related neural mechanisms. Search problems scale from individuals to societies, however, recent trends across disciplines indicate that the formal properties of these problems share similar structures and, often, similar solutions. Moreover, internal search (e.g., memory search) shows similar characteristics to external search (e.g., spatial foraging), including shared neural mechanisms consistent with a common evolutionary origin across species. For search scenarios, organisms must detect – and climb – noisy, long-range environmental (e.g., temperature, salinity, resource) gradients. Here,
social interactions can provide substantial additional benefit by allowing individuals, simply through grouping, to average their imperfect estimates of temporal and spatial cues (the so-called ‘
wisdom-of-crowds’ effect). Due to the investment necessary to obtain personal information, however, this again sets the scene for producers (searchers) to be exploited by others. == See also ==