Social search

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Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. [1] It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. [2] [3] [4] The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. [2] [5]

Contents

Social search may not be demonstrably better than algorithm-driven search. [6] In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. [7] Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. [8] [9] [10] [11]

Research and implementations

Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. [12] [13] Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. [14] This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles.

In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. [15] The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. [16] The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results.

In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. [17] However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. [18] Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. [19] By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. [20] In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. [20]

In December 2008, Twitter had re-introduced their people search feature. [21] While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine.

In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. [18]

Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. [22] In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. [23]

Social discovery

Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. [24] Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. [25] The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. [26] An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music [27] Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. [24]

Social search engines

A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. [28]

Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added.

An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. [29] In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aardvark algorithm forwards the question to someone in the asker extended social network who has the highest probability in knowing the answer to the question. Aadvark was obtained by Google in 2010 and abandoned later in 2011.

Potential drawbacks to social search lie in its open structure, as is the case with other tagged databases. As these are trust-based networks, unintentional or malicious misuse of tags in this context can lead to imprecise search results. There are number of social search engines that mainly based on tracking user information to order to provide related search results. Examples of this types are Smashfuse, SocialMention, Topsy and Social Searcher, [30] originally linked to Facebook. [31] Other versions of social engines have been launched, including Google Coop, Eurekster, Sproose, Rollyo, Anoox and Yahoo's MyWeb2.0.

Developments

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. [32]

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. [33] [34]

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. [35]

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. [36]

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. [37] [38]

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. [39] [40]

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. [41]

See also

Related Research Articles

<span class="mw-page-title-main">Google Search</span> Search engine from Google

Google Search is a search engine operated by Google. It allows users to search for information on the Internet by entering keywords or phrases. Google Search uses algorithms to analyze and rank websites based on their relevance to the search query. It is the most popular search engine worldwide.

In computing, a search engine is an information retrieval software system designed to help find information stored on one or more computer systems. Search engines discover, crawl, transform, and store information for retrieval and presentation in response to user queries. The search results are usually presented in a list and are commonly called hits. The most widely used type of search engine is a web search engine, which searches for information on the World Wide Web.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. SEO targets unpaid traffic rather than direct traffic or paid traffic. Unpaid traffic may originate from different kinds of searches, including image search, video search, academic search, news search, and industry-specific vertical search engines.

A backlink is a link from some other website to that web resource. A web resource may be a website, web page, or web directory.

<span class="mw-page-title-main">Content-based image retrieval</span> Method of image retrieval

Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches.

Federated search retrieves information from a variety of sources via a search application built on top of one or more search engines. A user makes a single query request which is distributed to the search engines, databases or other query engines participating in the federation. The federated search then aggregates the results that are received from the search engines for presentation to the user. Federated search can be used to integrate disparate information resources within a single large organization ("enterprise") or for the entire web.

A search engine results page (SERP) is a webpage that is displayed by a search engine in response to a query by a user. The main component of a SERP is the listing of results that are returned by the search engine in response to a keyword query.

Google Personalized Search is a personalized search feature of Google Search, introduced in 2004. All searches on Google Search are associated with a browser cookie record. When a user performs a search, the search results are not only based on the relevance of each web page to the search term, but also on which websites the user visited through previous search results. This provides a more personalized experience that can increase the relevance of the search results for the particular user. Such filtering may also have side effects, such as the creation of a filter bubble.

Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.

GenieKnows Inc. was a privately owned vertical search engine company based in Halifax, Nova Scotia. It was started by Rami Hamodah who also started SwiftlyLabs.com and Salesboom.com. Like many internet search engines, its revenue model centers on an online advertising platform and B2B transactions. It focuses on a set of search markets, or verticals, including health search, video games search, and local business directory search.

Collaborative search engines (CSE) are Web search engines and enterprise searches within company intranets that let users combine their efforts in information retrieval (IR) activities, share information resources collaboratively using knowledge tags, and allow experts to guide less experienced people through their searches. Collaboration partners do so by providing query terms, collective tagging, adding comments or opinions, rating search results, and links clicked of former (successful) IR activities to users having the same or a related information need.

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.

Search neutrality is a principle that search engines should have no editorial policies other than that their results be comprehensive, impartial and based solely on relevance. This means that when a user types in a search engine query, the engine should return the most relevant results found in the provider's domain, without manipulating the order of the results, excluding results, or in any other way manipulating the results to a certain bias.

Personalized search is a web search tailored specifically to an individual's interests by incorporating information about the individual beyond the specific query provided. There are two general approaches to personalizing search results, involving modifying the user's query and re-ranking search results.

<span class="mw-page-title-main">Filter bubble</span> Intellectual isolation involving search engines

A filter bubble or ideological frame is a state of intellectual isolation that can result from personalized searches, recommendation systems, and algorithmic curation. The search results are based on information about the user, such as their location, past click-behavior, and search history. Consequently, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles, resulting in a limited and customized view of the world. The choices made by these algorithms are only sometimes transparent. Prime examples include Google Personalized Search results and Facebook's personalized news-stream.

The following outline is provided as an overview of and topical guide to search engines.

<span class="mw-page-title-main">Facebook Graph Search</span> Semantic search engine by Facebook

Facebook Graph Search was a semantic search engine that Facebook introduced in March 2013. It was designed to give answers to user natural language queries rather than a list of links. The name refers to the social graph nature of Facebook, which maps the relationships among users. The Graph Search feature combined the big data acquired from its over one billion users and external data into a search engine providing user-specific search results. In a presentation headed by Facebook CEO Mark Zuckerberg, it was announced that the Graph Search algorithm finds information from within a user's network of friends. Microsoft's Bing search engine provided additional results. In July it was made available to all users using the U.S. English version of Facebook. After being made less publicly visible starting December 2014, the original Graph Search was almost entirely deprecated in June 2019.

Hummingbird is the codename given to a significant algorithm change in Google Search in 2013. Its name was derived from the speed and accuracy of the hummingbird. The change was announced on September 26, 2013, having already been in use for a month. "Hummingbird" places greater emphasis on natural language queries, considering context and meaning over individual keywords. It also looks deeper at content on individual pages of a website, with improved ability to lead users directly to the most appropriate page rather than just a website's homepage.

Local search engine optimization is similar to (national) SEO in that it is also a process affecting the visibility of a website or a web page in a web search engine's unpaid results often referred to as "natural", "organic", or "earned" results. In general, the higher ranked on the search results page and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users; these visitors can then be converted into customers. Local SEO, however, differs in that it is focused on optimizing a business's online presence so that its web pages will be displayed by search engines when users enter local searches for its products or services. Ranking for local search involves a similar process to general SEO but includes some specific elements to rank a business for local search.

References

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