Social information processing

Last updated October 24, 2023

Social information processing is "an activity through which collective human actions organize knowledge."^[1] It is the creation and processing of information by a group of people. As an academic field Social Information Processing studies the information processing power of networked social systems.

Authoring tools: e.g., blogs
Collaboration tools: e.g., wikis, in particular, e.g., Wikipedia
Translating tools: Duolingo, reCAPTCHA
Tagging systems (social bookmarking): e.g., del.icio.us, Flickr, CiteULike
Social networking: e.g., Facebook, MySpace, Essembly
Collaborative filtering: e.g., Digg, the Amazon Product Recommendation System, Yahoo! Answers, Urtak

Although computers are often used to facilitate networking and collaboration, they are not required. For example the Trictionary in 1982 was entirely paper and pen based, relying on neighborhood social networks and libraries. The creation of the Oxford English Dictionary in the 19th century was done largely with the help of anonymous volunteers organized by help wanted ads in newspapers and slips of paper sent through the postal mail.

Current state of knowledge

The website for the AAAI 2008 Spring Symposium on Social Information Processing suggested the following topics and questions:^[2]

Tagging: Tagging has already attracted the interest of the AI community. While the initial purpose of tagging was to help users organize and manage their own documents, it has since been proposed that collective tagging of common documents can be used to organize information via an informal classification system dubbed a folksonomy. There is hope^{[ who? ]} that folksonomies will eventually help fulfill the promise of the Semantic Web.

Human-based computation and collective intelligence: What type of problems are amenable to human swarm computing approaches? How can we design the "wisdom of crowds" effect to benefit our problem solving needs?

Incentives to participation: How to elicit quality metadata and content from users? How can users resistant to tagging be encouraged to tag content?

Social networks: While users create social networks for a variety of reasons – e.g., to track lives of friends or work or opinions of the users they respect – network information is important for many applications. Globally, an information ecosystem may arise through the interactions among users, and between users and content. A community of users interested in a specific topic may emerge over time, with linkages to other communities giving insight into relationships between topics.

Evolution of social media and information ecosystems: How does content, and its quality, change in time? There is increasing interest in peer-production systems, for example in how and why some open-source projects like Linux and Wikipedia are successful. Under what circumstances are user-generated content sites likely to succeed and what implications does this have for information-sharing and learning within communities?

Algorithms: Before we can harness the power of the social information processing, we need new approaches to structured data analysis, specifically algorithms for synthesizing various types of metadata: e.g., social networks and tagging. Research in this area will provide a principled foundation for the development of new algorithms for social search, information discovery and personalization and other approaches that exploit the power of the social information processing.

Key concepts

Social Recommender Systems

Social overload corresponds to being imposed to high amount of information and interaction on social web. Social overload causes some challenges from the aspect of both social media websites and their users.^[3] Users need to deal with high volume of information and to make decisions among different social network applications whereas social network sites try to keep their existing users and make their sites interesting to users. To overcome social overload, social recommender systems has been utilized to engage users in social media websites in a way that users receive more personalized content using recommendation techniques.^[3] Social recommender systems are specific types of recommendation systems being designed for social media and utilizing new sort of data brought by it, such as likes, comments, tags and so on, to improve effectiveness of recommendations. Recommendation in social media have several aspects like recommendation of social media content, people, groups and tags.

Content

Social media lets users to provide feedback on the content produced by users of social media websites, by means of commenting on or liking the content shared by others and annotating their own-created content via tagging. This newly introduced metadata by social media helps to obtain recommendations for social media content with improved effectiveness.^[3] Also, social media lets to extract the explicit relationship between users such as friendship and people followed/followers. This provides further improvement on collaborative filtering systems because now users can have judgement on the recommendations provided based on the people they have relationships.^[3] There have been studies showing the effectiveness of recommendation systems which utilize relationships among users on social media compared to traditional collaborative filtering based systems, specifically for movie and book recommendation.^[4]^[5] Another improvement brought by social media to recommender systems is solving the cold start problem for new users.^[3]

Some key application areas of social media content recommendation are blog and blog post recommendation, multimedia content recommendation such as YouTube videos, question and answer recommendation to question askers and answerers on social question-and-answer websites, job recommendation (LinkedIn), news recommendation on social new aggregator sites (like Digg, GoogleReader, Reddit etc.), short message recommendations on microblogs (such as Twitter).^[3]

People

Also known as social matching (the term is proposed by Terveen and McDonald), people recommender systems deal with recommending people to people on social media. Aspects making people recommender systems distinct from traditional recommender systems and require special attention are basically privacy, trust among users, and reputation.^[6] There are several factors which effect the choice of recommendation techniques for people recommendation on social networking sites (SNS). Those factors are related to types of relationships among people on social networking sites, such as symmetric vs asymmetric, ad-hoc vs long-term, and confirmed vs nonconfirmed relationships.^[3]

The scope of people recommender systems can be categorized into three:^[3] recommending familiar people to connect with, recommending people to follow and recommending strangers. Recommending strangers is seen as valuable as recommending familiar people because of leading to chances such as exchanging ideas, obtaining new opportunities, and increasing one’s reputation.

Challenges

Handling with social streams is one of the challenges social recommender systems face with.^[3] Social stream can be described as the user activity data pooled on newsfeed on social media websites. Social stream data has unique characteristics such as rapid flow, variety of data (only text content vs heterogenous content), and requiring freshness. Those unique properties of stream data compared to traditional social media data impose challenges on social recommender systems. Another challenge in social recommendation is performing cross-domain recommendation, as in traditional recommender systems.^[3] The reason is that social media websites in different domains include different information about users, and merging information within different contexts may not lead to useful recommendations. For example, using favorite recipes of users in one social media site may not be a reliable source of information to effective job recommendations for them.

Social awareness

Participation of people in online communities, in general, differ from their participatory behavior in real-world collective contexts. Humans in daily life are used to making use of "social cues" for guiding their decisions and actions e.g. if a group of people is looking for a good restaurant to have lunch, it is very likely that they will choose to enter to a local that have some customers inside instead of one that it is empty (the more crowded restaurant could reflect its popularity and in consequence, its quality of service). However, in online social environments, it is not straightforward how to access to these sources of information which are normally being logged in the systems, but this is not disclosed to the users.

There are some theories that explain how this social awareness can affect the behavior of people in real-life scenarios. The American philosopher George Herbert Mead states that humans are social creatures, in the sense that people's actions cannot be isolated from the behavior of the whole collective they are part of because every individuals' act are influenced by larger social practices that act as a general behavior's framework.^[7] In his performance framework, the Canadian sociologist Erving Goffman postulates that in everyday social interactions individuals perform their actions by collecting information from others first, in order to know in advance what they may expect from them and in this way being able to plan how to behave more effectively.^[8]

Benefits

In the same way that in the real-world, providing social cues in virtual communities can help people to understand better the situations they face in these environments, to alleviate their decision-making processes by enabling their access to more informed choices, to persuade them to participate in the activities that take place there, and to structure their own schedule of individual and group activities more efficiently.^[9]

In this frame of reference, an approach called "social context displays" has been proposed for showing social information -either from real or virtual environments- in digital scenarios. It is based on the use of graphical representations to visualize the presence and activity traces of a group of people, thus providing users with a third-party view of what is happening within the community i.e. who are actively participating, who are not contributing to the group efforts, etc. This social-context-revealing approach has been studied in different scenarios (e.g. IBM video-conference software, large community displaying social activity traces in a shared space called NOMATIC*VIZ), and it has been demonstrated that its application can provide users with several benefits, like providing them with more information to make better decisions and motivating them to take an active attitude towards the management of their self and group representations within the display through their actions in the real-life.^[9]

Concerns

By making the traces of activity of users publicly available for others to access it is natural that it can raise users concerns related to which are their rights over the data they generate, who are the final users that can have access to their information and how they can know and control their privacy policies.^[9] There are several perspectives that try to contextualize this privacy issue. One perspective is to see privacy as a tradeoff between the degree of invasion to the personal space and the number of benefits that the user could perceive from the social system by disclosing their online activity traces.^[10] Another perspective is examining the concession between the visibility of people within the social system and their level of privacy, which can be managed at an individual or at a group level by establishing specific permissions for allowing others to have access to their information. Other authors state that instead of enforcing users to set and control privacy settings, social systems might focus on raising their awareness about who their audiences are so they can manage their online behavior according to the reactions they expect from those different user groups.^[9]

Related Research Articles

Personal information management (PIM) is the study and implementation of the activities that people perform in order to acquire or create, store, organize, maintain, retrieve, and use informational items such as documents, web pages, and email messages for everyday use to complete tasks and fulfill a person's various roles ; it is information management with intrapersonal scope.

Computer-supported cooperative work (CSCW) is the study of how people utilize technology collaboratively, often towards a shared goal. CSCW addresses how computer systems can support collaborative activity and coordination. More specifically, the field of CSCW seeks to analyze and draw connections between currently understood human psychological and social behaviors and available collaborative tools, or groupware. Often the goal of CSCW is to help promote and utilize technology in a collaborative way, and help create new tools to succeed in that goal. These parallels allow CSCW research to inform future design patterns or assist in the development of entirely new tools.

Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

A recommender system, or a recommendation system, is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Social computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. Thus, blogs, email, instant messaging, social network services, wikis, social bookmarking and other instances of what is often called social software illustrate ideas from social computing.

Computer-supported collaboration research focuses on technology that affects groups, organizations, communities and societies, e.g., voice mail and text chat. It grew from cooperative work study of supporting people's work activities and working relationships. As net technology increasingly supported a wide range of recreational and social activities, consumer markets expanded the user base, enabling more and more people to connect online to create what researchers have called a computer supported cooperative work, which includes "all contexts in which technology is used to mediate human activities such as communication, coordination, cooperation, competition, entertainment, games, art, and music".

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

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. 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. 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.

Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.

Targeted advertising is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting. These traits can either be demographic with a focus on race, economic status, sex, age, generation, level of education, income level, and employment, or psychographic focused on the consumer values, personality, attitude, opinion, lifestyle and interest. This focus can also entail behavioral variables, such as browser history, purchase history, and other recent online activities. The process of algorithm targeting eliminates waste.

Expertise finding is the use of tools for finding and assessing individual expertise. In the recruitment industry, expertise finding is the problem of searching for employable candidates with certain required skills set. In other words, it is the challenge of linking humans to expertise areas, and as such is a sub-problem of expertise retrieval.

Social translucence is a term that was proposed by Thomas Erickson and Wendy Kellogg to refer to "design digital systems that support coherent behavior by making participants and their activities visible to one another".

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.

Urban computing is an interdisciplinary field which pertains to the study and application of computing technology in urban areas. This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas. Urban computing is the technological framework for smart cities.

Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information. The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information, and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.

A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as ‘accounts’, allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system.

Social media mining is the process of obtaining big data from user-generated content on social media sites and mobile apps in order to extract actionable patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research. The term is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to shift through vast quantities of raw ore to find the precious minerals; likewise, social media mining requires human data analysts and automated software programs to shift through massive amounts of raw social media data in order to discern patterns and trends relating to social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, and more. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs, new products, processes or services.

Social navigation is a form of social computing introduced by Paul Dourish and Matthew Chalmers in 1994, who defined it as when "movement from one item to another is provoked as an artifact of the activity of another or a group of others". According to later research in 2002, "social navigation exploits the knowledge and experience of peer users of information resources" to guide users in the information space, and that it is becoming more difficult to navigate and search efficiently with all the digital information available from the World Wide Web and other sources. Studying others' navigational trails and understanding their behavior can help improve one's own search strategy by guiding them to make more informed decisions based on the actions of others.

Social profiling is the process of constructing a social media user's profile using his or her social data. In general, profiling refers to the data science process of generating a person's profile with computerized algorithms and technology. There are various platforms for sharing this information with the proliferation of growing popular social networks, including but not limited to LinkedIn, Google+, Facebook and Twitter.

References

↑ AAAI (March 2008), Social Information Processing, AAAI Spring Symposium, Stanford University{{citation}}: CS1 maint: location missing publisher (link)
↑ "Social Information Processing Symposium".
1 2 3 4 5 6 7 8 9 10 Guy, Ido (1 January 2015). "Social Recommender Systems". Recommender Systems Handbook. pp. 511–543. doi:10.1007/978-1-4899-7637-6_15. ISBN 978-1-4899-7636-9.
↑ Sinha, Rashmi; Swearingen, Kirsten (2001). "Comparing Recommendations Made by Online Systems and Friends". DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries. 106.
↑ Golbeck, Jennifer (2006-05-16). "Generating Predictive Movie Recommendations from Trust in Social Networks". Trust Management. Lecture Notes in Computer Science. Vol. 3986. pp. 93–104. doi:10.1007/11755593_8. ISBN 978-3-540-34295-3.
↑ Terveen, Loren; McDonald, David W. (2005-09-01). "Social Matching: A Framework and Research Agenda". ACM Trans. Comput.-Hum. Interact. 12 (3): 401–434. doi:10.1145/1096737.1096740. ISSN 1073-0516. S2CID 6481687.
↑ Mead, George. H. (1934). Mind, Self, and Society: From the Standpoint of a Social Behaviorist . Chicago: University of Chicago Press.
↑ Goffman, Erving (1990). The presentation of self in everyday life. London: Penguin. ISBN 978-0-14-013571-8.
1 2 3 4 Ding, Xianghua; Erickson, Thomas; Kellogg, Wendy A.; Patterson, Donald J. (2011). "Informing and performing: investigating how mediated sociality becomes visible". Personal and Ubiquitous Computing. 16 (8): 1095–1117. doi:10.1007/s00779-011-0443-8. ISSN 1617-4909. S2CID 1121330.
↑ Patil, Sameer; Lai, Jennifer (2005). "Who gets to know what when: Configuring privacy permissions in an awareness application". Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 101–110. doi:10.1145/1054972.1054987. ISBN 978-1581139983. S2CID 12102947.