Results and implications
Social networking sites (SNSs) are technological innovations that have received widespread attention from practitioners and researchers (Ngai, et al., 2015; Caers, et al., 2013), being used for interpersonal communication and collaboration among users (Kane, et al., 2014). In addition to their technological impact, there are cultural influences of SNSs on societies (boyd, 2015). Furthermore, the rapid dissemination of information by SNSs has influenced society in terms of psychological well-being, political participation, civic engagement and online journalism, to name a few (Kümpel, et al., 2015). SNSs have been classified into seven different types — public, business, content, target, domain, activity and micro (Richter, et al., 2009). These communication tools require little or no computing skills in order to post user-generated content (Wilson, et al., 2012).
User-generated content is defined as those resources in the form of text, media or metadata that are posted by users either intentionally or impulsively on SNSs (Kane, et al., 2014; Shim, et al., 2013). Individuals and communities co-create and share user-generated content which is used widely in different situations, such as terrorist attacks, product recalls or café shooting incidents (Oh, et al., 2013). Thus, a wide variety of user-generated content is posted by users on SNSs, facilitating social interaction and communication among users (Richthammer, et al., 2014). Benefits of SNSs include news sharing (Kümpel, et al., 2015), convenience in maintaining relationships (Krasnova, et al., 2010), self-presentation (Spertus, et al., 2005), searching and finding old friends, facilitate entertainment and self-satisfaction by helping others (Kim, et al., 2010). From an altruistic perspective, SNSs are used to find the owners of lost items by posting content on online communities, and used as tools for expressing human rights concerns, freedom of speech and democracy (McGrath, et al., 2012; Kim, et al., 2010). In yet another context, SNSs support charity initiatives, popularised environmental concerns and support the visibility of political candidates (Vergeer, et al., 2013; Robelia, et al., 2011). Thus, there are numerous benefits of SNSs for users.
On the other hand, SNSs can cause users’ harm and harm to others (Patchin and Hinduja, 2010), entail security implications (Kim, et al., 2010), and have legal and privacy issues (Stutzman, et al., 2012). Representing real identities on SNSs may cause privacy risk issues (Brooks and Anene, 2012), identity theft (Fogel and Nehmad, 2009), spamming (Zinman and Donath, 2007), personal assault (Patchin and Hinduja, 2010), and risk of burglary (Peterson and Siek, 2009). Other negative outcomes of SNSs include fake online identities, cyber bullying, cyber stalking and spreading false information (boyd and Ellison, 2008; Gross and Acquisti, 2005).
To exemplify the uses, social networking sites have been used widely in higher education (Erskine, et al., 2014); government (Cumbie and Kar, 2014); physician-patient interactions (Dantu, et al., 2014); e-democracy (Finau, et al., 2014); emergency management (Gill, et al., 2014); innovation (Ooms, et al., 2015) and in organisations (Lattemann, et al., 2014; Parveen, et al., 2015). Though SNSs have been used widely in online communication (Papagiannidis and Bourlakis, 2015), organisations have adopted SNSs primarily to accomplish two level of functionalities, i.e., external and internal uses. In external use, organisations receive ideas or recommendations to improve their existing products or develop new products. In internal use, groups are created to communicate within an organisation (Bjelland, 2008). Examples of internal use include IBM’s Beehive (Turban, et al., 2011) and external use includes Procter and Gamble’s Connect and Develop platform (Chesbrough, 2006). A CEO’s apology on YouTube following a service failure won the hearts of customers and proved the benefits of a SNS in an organisational context (Manika, et al., 2015). Furthermore, research on the widespread impact of SNSs on organisations (Parveen, et al., 2015) and individuals (Nosko, et al., 2010; Ahmed, et al., 2014) is growing.
The rest of this paper is structured as follows. Literature on user-generated content is introduced first which leads to the development of an information classification framework. This is followed by research questions, methodology, methods of data collection and coding. Then, results and implications of user-generated content posted by users are presented. Limitations of the study and future work are discussed in the conclusion.
Review of various studies on user-generated content in the context of profile-related content, profession-related content and social context are discussed below.
Profile related content posted by users range from personal data (name, photo, birthday, gender) to relationship details, achievements, hobbies, interests, e-mail addresses and phone numbers (Stutzman, 2006). According to Kim, et al. (2010) SNSs include a user profile which features basic information (name, photo, age, birthday, relationship status, gender, type of relationship), personal information (interest, favourite music, TV shows, movies, books, and quotation), contact information (mobile phone, land phone, address) and education and work information (school attended, employer details). Thelwall (2008) extended profile content to include marital status, which is also supported by Skeels and Gurdin (2009), who added religious status and political party membership information as well. User profile content disclosed on SNSs was grouped into standard, sensitive and potentially stigmatizing information by Nosko, et al. (2010) to examine identity threats (impersonation). Standard information generally includes gender, birthday, birth year, e-mail and profile picture, while sensitive information comprises messages, photos and albums (Nosko, et al., 2010). Meanwhile, stigmatizing information consists of religious views, political views, sexual orientation, and quotes (Nosko, et al., 2010). SNSs have facilitated young adults to express themselves and create online identities by sharing different types of personal information (Pempek, et al., 2009). Users of SNSs can be connected with schools and with details of their specific courses on their user profiles (Zywica and Danowski, 2008). Other types of information shared by users may be education background, academic interests and achievements, messages, locations, profile photographs, honours, awards and extracurricular accomplishments (Zywica and Danowski, 2008). Individual taste of users on SNSs in terms of hobbies and interests, such as music, books, movies and television shows, in addition to general interests, such as camping, painting or hiking, were studied by Liu (2007) by examining personal information shared by users on their SNS profiles.
This research indicates that user profile information posted on SNSs comprises of names, birthdays, gender, profile pictures, e-mail addresses (Dwyer, et al., 2007), contact information, marital status, religious views, political views, language proficiencies (Nosko, et al., 2010; Kim, et al., 2010), honours and awards (Stutzman, 2006; Zywica and Danowski, 2008), and interests (Liu, 2007; Zhao, et al., 2008). Literature (Richthammer, et al., 2014; Dwyer, et al., 2007; Nosko, et al., 2010) on SNS data types indicates that semantically specified content on SNSs include mandatory content and optionally provided content. Mandatory content is the information that users are required to provide in order to successfully enroll on SNSs, such as names, birthdays, gender, and e-mail addresses. Optionally provided content is non-obligatory information that users may elect to provide (Gross and Acquisti, 2005; Skeels and Grudin, 2009; Herring, 2007) such as contact information, marital status, religious views, political views, language proficiencies, profile pictures, honours, awards, and interests. Since mandatory content and optionally provided content provide basic details about SNS users (Nosko, et al., 2010), they are coalesced as ‘user information’.
The literature (Stutzman, 2006; Zywica and Danowski, 2008) also indicate that user profile information could include class schedules, schools attended, honors and awards, courses taken, and qualifications. These second set of optionally provided content have been classified under content that have concise meaning (Richthammer, et al., 2014). Hence graduation year, school attended, class schedules, courses taken, and qualifications which are related to educational details of SNS users (Kim, et al., 2010; Nosko, et al., 2010) are coalesced under ‘user education’.
Profession-related content includes positions held, professional experience, professional affiliations, professional contacts, and employment status (Papacharissi, 2009). Job titles, job descriptions, job start dates, number of company friends, job-related groups (internship groups), resumes, and job descriptions are different types of content users post on SNSs (DiMicco and Millen, 2007). To add on, career histories and employer information were additional profession-related content posted by users on SNSs (Case, et al., 2013). Other profession-related content includes types of job opportunities available and career progression from technical to managerial positions over time. Users’ share professional information such as project updates with colleagues and are motivated to connect with colleagues at a personal level and advance their career within an organisation (DiMicco, et al., 2008).
Previously cited literature indicates that profession-related content posted on SNSs include professional skills, interests, job descriptions, job start dates, positions held, resumes (DiMicco and Millen, 2007; DiMicco, et al., 2008; Papacharissi, 2009), graduation dates, career histories, job titles, employer information, and professional affiliations (Case, et al., 2013). Thus, a third set of optionally provided content classified under data with concise meaning (Richthammer, et al., 2014) is career histories, job titles, employer information, job descriptions, job start dates, resumes, positions held, professional affiliations, and professional skills. Since this optionally provided content is related to employment details of SNS users (Case, et al., 2013), they are coalesced as ‘user employment’.
The types of social information posted by users may be praise, jokes, greetings, photos, and videos to share users’ identities (Manago, et al., 2008) whereas comments or wall posts are types of user-generated content that facilitate social interaction. Collaboration on SNSs leads to content such as personal diaries (life details), advertisements, and announcements (Kaplan and Haenlein, 2010). According to Honeycutt and Herring (2009), collaboration on Twitter involves different types of content such as self-experience or reflections, announcements, opinions, greetings and other miscellaneous information or text that does not convey meaningful information. Other types of information posted on SNSs are complaints, random thoughts and user activities or diaries (Naaman, et al., 2010). On the other hand, some users post cartoons on SNSs to criticise or influence a political decision-making (Cranefield and Oliver, 2014). According to Binder, et al. (2009) criticism, rumors, and gossip resulted in the generation of online tension among users. Hashtags are types of user-generated content that can be used to direct posts to individuals or events (Conole and Culver, 2009). Users also post different types of photos and videos on SNSs which include vacation photos, project photos, variety of photos (DiMicco, et al., 2008), party photos (Peluchette and Karl, 2008) and videos (Lange, 2007).
The types of information that users post on SNSs in the social content category are status updates (Nosko, et al., 2010), invitations, recommendations (Morris, et al., 2010), diaries, random thoughts (Naaman, et al., 2010), requests (Golbeck, et al., 2010), gossip, criticism (Binder, et al., 2009), announcements, greetings, opinions, self-experience (Honeycutt and Herring, 2009), hashtags (Conole and Culver, 2009), creative writing (Kaplan and Haenlein, 2010), cartoons (Cranefield and Oliver, 2014), praise (Manago, et al., 2008), photos (DiMicco, et al., 2008; Peluchette and Karl, 2008) and videos (Lange, 2007). According to Richthammer, et al. (2014), the above disclosed content posted on SNSs do not have succinct or clearly defined meaning. Further, in the disclosed content category, user-generated content are of three types: text, video (Kaplan and Haenlein, 2010; DiMicco, et al., 2008), photos (Kaplan and Haenlein, 2010) or cartoons (Cranefield and Oliver, 2014). Thus, content is represented either through textual mode (Abbasi and Chen, 2008) or using rich media (Dennis, et al., 2008). Hence party photos, vacation photos, project photos, variety of photos, cartoons, and videos are classified as ‘media rich information’ due to the nature of content.
On the other hand, announcements, creative writing, invitations, status updates, requests, greetings, praise, recommendations, diaries, opinions, random thoughts, gossip, criticism, self-experience, quotations and tags discussed in this section are classified as ‘textual communication’ due to the nature of content.
Thus, the basic user-generated content illustrated above are coalesced under five sub-categories which are user information, user education, user employment, media rich information, and textual communication. According to Tow, et al. (2010), names, relationship status, friends list, photos, dates of birth, and education history are classified under personal information. Thus, the sub-categories: user information and user education are classified as personal information. According to Najmul Islam and Mäntymäki (2012) and Skeels and Grudin (2009), professional information includes curriculum vitae and other professional content. Hence, user employment is classified as professional information. According to Quan-Haase and Young (2010), social information includes activities and events of network members as well as those messages that are important in a community. Hence, media rich information and textual communication are classified as social information. The information classification framework of user-generated content from basic content to broader categories of information is illustrated below.
The aim of this study is to explore the different types of user-generated content posted by users on Facebook and to establish the implications (user benefits and costs) to users. Thus, the research questions that this study will address are:
- What are the different types of user-generated content posted by users on the Facebook page of organisations?
- What are the implications of user-generated content to individuals and organisations?
This is a qualitative research; the methodology used is inductive content analysis. Previous studies have used content analysis to collect and analyse data from Web sites (Steininger, et al., 2011) and SNSs (Shelton and Skalski, 2014). This methodology was chosen since the implications of different types of user-generated content posted on SNSs is yet to be established. The focus is on public postings on the Facebook pages of two organisations. Thematic analysis (Braun and Clarke, 2006) was used to establish themes relevant to user benefits and costs. To limit the scope, this study considered only content that was in the form of text, posted by users on Facebook.
Data collection and coding
To conduct an interpretive analysis of content, one of the primary criteria is to collect rich subjective information (Walsham and Sahay, 1999). Only Facebook satisfied this criterion of rich content when compared with other SNSs (Nosko, et al., 2010). An individual user account was created on Facebook to collect data. Publicly accessible content posted by users of two popular organisations on Facebook, one in the context of open access institutional repositories (dataset 1) and another one in the context of emergency management (dataset 2) was selected for data collection. For the first dataset, user posts during a seven-year period (2007–2014) were collected; for the second dataset, user posts during a six-month period (January to June 2015) were collected. Following pre-processing methodologies in other studies (Gordon, et al., 2013), we adopted a similar approach for collecting and pre-processing raw data in a Microsoft word document. Since this study considers only text postings of users, images and videos were not collected for coding due to the contextual nature of media-rich information.
Posts were saved as PDF documents and loaded into NVivo 10 for coding. Nodes were created on NVivo for different types of user-generated content as noted in the information classification framework. The collected posts were pre-processed to remove content that was not understandable in terms of symbols; text in a language other than English; or text that was not relevant to open access institutional repositories or emergency management. Thus, for the first dataset, 162 posts were found to be relevant for coding; for the second dataset, 944 posts were chosen for coding.
Pre-processed posts were read and analysed in order to secure detailed insight into the nature of the posts. Each user post was examined and then coded with relevant user-generated content from the information classification framework (Figure 6). Lengthy posts were read thoroughly and coded succinctly under relevant user-generated content. After the first round of coding, clarifications that arose were discussed with another researcher who holds a research active university classification and specialised in social media research. This resulted in a second round of coding as a mutual agreement on coding was accomplished. These steps were followed to ensure the rigour in the research process (Sarker, et al., 2013).
Results and implications
Analysis of data indicates that major types of user-generated content identified from the first dataset on open access institutional repository were requests, greetings, status updates and announcements, accounting for 70 percent of total posts. On the other hand, major types of user-generated content identified from the second dataset on emergency management were status updates, criticism and requests, equaling 89 percent of total posts. The common types of user-generated content posted by users on both datasets were requests and status updates.
According to Morris, et al. (2010), a request is a service sought from network connections and is a type of user-generated content in the social information category of the information classification framework. A majority of requests from open access repository users were of a professional nature. These include technical requests such as customizing interfaces or requests to participate in the trial of new services. On the other hand, the majority of requests from emergency management were of a social nature. These include requests to help a family in an emergency or requests to reconsider the payment of flood insurance on installments. Thus, it is evident that requests can be of a professional or social nature. Professional implications include technical assistance, supporting projects that extend open access repository initiatives as well as collaboration and building capacity among repository users. Social implications were accomplished in terms of the first, second, third, sixth and seventh core principles of the National Strategy for Disaster Resilience (Council of Australian Governments, 2011). Information seeking and relationship building were implied in these requests, in consensus with Case (2012) and Krasnova, et al. (2010). Information seeking is also in accord with the study of Kim, et al. (2014) on everyday life information seeking using social media.
Status updates, in the social information category, notify others about users’ actions, thoughts and feelings (Nosko, et al., 2010). In the dataset of open access institutional repository users, status updates were used to communicate technical updates, user repository activities or user meetings to network connections. In the context of emergency management, status updates were applied to disseminate information about a proposal to start a response team, volunteering for a task, or extending help in an emergency. Thus, it is evident that status updates can be of professional or social nature. Professional implications include user community development, marketing and communication whereas social implications concur with the core principles of the National Strategy for Disaster Resilience. With respect to theoretical implications, analysis of status update information is coordination, in consensus with an earlier general study (Jarzabkowski, et al., 2012). Moreover, sharing information to network connections is in accord with Kaplan and Haenlein (2010) on information dissemination. Further, sharing technical updates leads to disseminating knowledge which is in consensus with Kim, et al. (2010) as well as Caers, et al. (2013) on disseminating knowledge in a scientific context.
According to Kaplan and Haenlein (2010), criticism is defined as an expression of disapproval. Among the different types of user-generated content, criticism was not significant in the dataset of open access institutional repository users. In the context of emergency management, criticism indicates a lack of interest among some officials in conducting emergency preparedness programs, insufficient support after an emergency, lack of support extended for coordinated efforts, inefficient use of public funds in an emergency and unreasonable flood insurance premium. Thus, it was evident that implications derived from criticism were of social nature. These social implications concur with the first, second, third, sixth and seventh core principles of the National Strategy for Disaster Resilience. Social conflict was in accord with the study of Koroleva, et al. (2011).
A greeting is defined as a note of welcome posted by users on SNS user profiles (Chun, et al., 2008). Among the different types of user-generated content, greetings were not significant in the emergency management dataset. In the context of open access repository users, greetings were posted at the beginning of requests, for receiving help and in anticipation of specific help. Thus, it was evident that in a professional context greetings are of social nature. Greetings are part of community development as well as relationship building, in agreement with Salmon (2003) and Renzi and Klobas (2002).
An announcement is a way of communicating information on social media (Honeycutt and Herring, 2009). Among different types of user-generated content, announcements were not significant in the emergency management dataset. In the context of open access repository users, announcements included information on new repository functionalities, research accomplishments, conferences, scholarships, jobs and newsletters. Thus, announcements were of professional nature, contributing to community development, marketing and communication. This sort of knowledge dissemination was also found by Kim, et al. (2010).
Data analysis indicated that a majority of content shared on Facebook was social rather than personal or professional information. It was also evident that implications of this information could be professional or social in nature, consistent with Lampinen, et al. (2009). The classification scheme, illustrated in Figure 6, is an initial step to determine the nature of user-generated content and to examine its implications in terms of benefits and costs.
Social interactions on SNSs lead to the creation of digital identities (Tajfel and Turner, 1979; Simon, 2008). Information posted by users on SNSs appears in two modes — profile information and activity information (Zhang, et al., 2010). Profile information includes succinct information such as name or date of birth which have a semantically specified meaning, whereas activity information includes contextual information, such as comments. Hum, et al. (2011) studied Facebook profile photos and further established identity as a result of posts. Furthermore, identity has been defined in terms of explicit and implicit identity (Zhao, et al., 2008). Explicit identity is a result of posting profile information, whereas implicit identity was accomplished by posting activities or conversations. Thus, users establish social identity by posting information or joining groups on SNSs (Pempek, et al., 2009). Moreover, one of the key implications for users to post or restrict information on SNSs was identity construction (Stern and Taylor, 2007). Thus, users post information to construct identities on SNSs, in agreement with DiMicco and Millen (2007) on impression management.
According to Homans (1958), social exchange theory posits that interaction among participants, referred to as social interaction, leads to rewards and costs. Rewards result in the creation of positive value for participants whereas costs lead to negative value. Participants are more likely to engage in actions that result in the creation of positive value or rewards leading to benefits. These notions are in consensus with relationship building found in this study, in agreement with results noted in Shin and Hall (2012). The findings of this study also demonstrate that social conflict is a major cost, in accord with social exchange theory. The findings of relationship building and social conflict discovered in this study concur with rewards and costs of social interactions supported by social exchange theory.
Data collected and analysed in this study were publicly accessible on Facebook for two organisations, which may be considered a limitation. Certain kinds of information may be more widely posted privately. In addition, the proposed information classification framework described in this paper has only been tested with content from Facebook, certainly another limitation. To develop a more generally applicable framework, it would be necessary to test this scheme with a wide variety of social networking sites.
This study examined user-generated content on Facebook from two organisations. User posts were coded into categories of personal, professional and social information. The findings of this study demonstrated that information seeking, relationship building, coordination and collaboration, identity construction and knowledge dissemination were the major benefits for posting content on Facebook. On the other hand, social conflict was a major cost to users on Facebook. For organisations, the benefits amounted to maximising technical assistance, supporting projects, collaboration and building capacity among users, community development, marketing and communication and addressing the core principles of the National Strategy for Disaster Resilience. A similar study on other social networking sites should establish the significance of user-generated content in a different context. Finally, there needs to be some reconfirmation of the conclusions of this study on the significance of user-generated content directly with users, such as utilizing interviews to confirm findings. Interviews may also educate users on the implications of their use of Facebook, in order to avoid negative ramifications and enhancing positive outcomes.
About the author
Jayan is a Lecturer at RMIT University, Vietnam. He has a M.Phil. from the University of Nottingham. His research interests are on open source repositories and social networking sites. Jayan has received funding from Google and DSpace Foundation and has successfully mentored Google Summer of Code projects in 2008 and 2009. In 2008, he received the eINDIA2008 award for his contribution to the open source community. In 2010, he was presented with the “Graduate Scholar Award” — Ubiquitous Learning Conference held at the University of British Columbia, Canada. Jayan can be contacted at jayan [dot] kurian [at] rmit [dot] edu [dot] vn
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Received 9 May 2015; revised 30 April 2016; accepted 23 May 2016.
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User-generated content on Facebook: Implications from the perspective of two organisations
by Jayan Kurian.
First Monday, Volume 21, Number 7 - 4 July 2016