Wikimedia Foundation Research Articles

Digital Cultural Heritage and Wikimedia Commons Licenses:

Cultural heritage institutions can contribute to public knowledge and increase awareness of their collections by uploading digital objects to Wikimedia Commons for use on Wikipedia and other Wikimedia Foundation projects. However, prior research has established the difficulty of and/or hesitation by many cultural heritage institutions in clearly and accurately labeling the copyright status of their born-digital and digitized collections. With this knowledge, how likely is it that digital cultural heritage will be findable and usable on Wikimedia Commons? This study seeks to determine how accurate rights statements for cultural heritage objects on Wikimedia Commons are, and whether inaccuracies can be linked to problematic rights statements in cultural heritage digital libraries or whether the inaccuracies stem from Wikimedia Commons. By evaluating the rights statements, licenses, and sources for 308 Wikimedia Commons objects from 57 cultural heritage organizations and comparing that information to corresponding licenses from digital libraries, we can begin to develop best practices and educational needs for digital librarians, archives, museum curators, and Wikipedians alike to improve the user experience for those using digital cultural heritage on Wikimedia projects.

Reuse of Wikimedia Commons Cultural Heritage Images on the Wider Web

Abstract
 Objective – Cultural heritage institutions with digital images on Wikimedia Commons want to know if and how those images are being reused. This study attempts to gauge the impact of digital cultural heritage images from Wikimedia Commons by using Reverse Image Lookup (RIL) to determine the quantity and content of different types of reuse, barriers to using RIL to assess reuse, and whether reused digital cultural heritage images from Wikimedia Commons include licensing information.
 Methods – 171 digital cultural heritage Wikimedia Commons images from 51 cultural heritage institutions were searched using the Google images “Search by image” tool to find instances of reuse. Content analysis of the digital cultural heritage images and the context in which they were reused was conducted to apply broad content categories. Reuse within Wikimedia Foundation projects was also recorded.
 Results – A total of 1,533 reuse instances found via Google images and Wikimedia Commons’ file usage reports were analyzed. Over half of reuse occurred within Wikimedia projects or wiki aggregator and mirror sites. Notable People, people, historic events, and buildings and locations were the most widely reused topics of digital cultural heritage both within Wikimedia projects and beyond, while social, media gallery, news, and education websites were the most likely places to find reuse outside of wiki projects. However, the content of reused images varied slightly depending on the website type on which they were found. Very few instances of reuse included licensing information, and those that did often were incorrect. Reuse of cultural heritage images from Wikimedia Commons was either done without added context or content, as in the case of media galleries, or was done in ways that did not distort or mischaracterize the images being reused.
 Conclusion – Cultural heritage institutions can use this research to focus digitization and digital content marketing efforts in order to optimize reuse by the types of websites and users that best meet their institution’s mission. Institutions that fear reuse without attribution have reason for concern as the practice of reusing both Creative Commons and public domain media without rights statements is widespread. More research needs to be conducted to determine if notability of institution or collection affects likelihood of reuse, as preliminary results show a weak correlation between number of images searched and number of images reused per institution. RIL technology is a reliable method of finding image reuse but is a labour-intensive process that may best be conducted for selected images and specific assessment campaigns. Finally, the reused content and context categories developed here may contribute to a standardized set of codes for assessing digital cultural heritage reuse.

Using Wikidata and Metaphactory to Underpin an Integrated Flora of Canada

We are using Wikidata and Metaphactory to build an Integrated Flora of Canada (IFC). IFC will be integrated in two senses: First, it will draw on multiple existing flora (e.g. Flora of North America, Flora of Manitoba, etc.) for content. Second, it will be a portal to related resources such as annotations, specimens, literature, and sequence data. Background We had success using Semantic Media Wiki (SMW) as the platform for an on-line representation of the Flora of North America (FNA). We used Charaparser (Cui 2012) to extract plant structures (e.g. “stem”), characters (e.g. “external texture”), and character values (e.g. “glabrous”) from the semi-structured FNA treatments. We then loaded this data into SMW, which allows us to query for taxa based on their character traits, and enables a broad range of exploratory analysis, both for purposes of hypothesis generation, and also to provide support for or against specific scientific hypotheses. Migrating to Wikidata/Wikibase We decided to explore a migration from SMW to Wikibase for three main reasons: simplified workflow; triple level provenance; and sustainability. Simplified workflow: Our workflow for our FNA-based portal includes Natural Language Processing (NLP) of coarse-grained XML to get the fine-grained XML, transforming this XML for input into SMW, and a custom SMW skin for displaying the data. We consider the coarse-grained XML to be canonical. When it changes (because we find an error, or we improve our NLP), we have to re-run the transformation, and re-load the data, which is time-consuming. Ideally, our presentation would be based on API calls to the data itself, eliminating the need to transform and re-load after every change. Provenance: Wikidata's provenance model supports having multiple, conflicting assertions for the same character trait, which is something that inevitably happens when floristic data is integrated. Sustainability: Wikidata has strong support from the Wikimedia Foundation, while SMW is increasingly seen as a legacy system. Wikibase vs. Wikidata Wikidata, however, is not a suitable home for the Integrated Flora of Canada. It is built upon a relatively small number of community curated properties, while we have ~4500 properties for the Asteraceae family alone. The model we want to pursue is to use Wikidata for a small group of core properties (e.g. accepted name, parent taxon, etc.), and to use our own instance of Wikibase for the much larger number of specialized morphological properties (e.g. adaxial leaf colour, leaf external texture, etc.) Essentially, we will be running our own Wikidata, over which we would exercise full control. Miller (2018) decribes deploying this curation model in another domain. Metaphactory Metaphactory is a suite of middleware and front-end interfaces for authoring, managing, and querying knowledge graphs, including mechanisms for faceted search and geospatial visualizations. It is also the software (together with Blazegraph) behind the Wikidata Query Service. Metaphactory provides us with a SPARQL endpoint; a templating mechanism that allows each taxonomic treatment to be rendered via a collection of SPARQL queries; reasoning capabilities (via an underlying graph database) that permit the organization of over 42,000 morphological properties; and a variety of search and discovery tools. There are a number of ways in which Wikidata and Metaphactory can work together, and we are still exploring questions such as: Will provenance be managed via named graphs, or via the Wikidata snak model?; How will data flow between the two platforms? Etc. We will report on our findings to date, and invite collaboration with related Wikimedia-based projects.

Using Crowd-curation to Improve Taxon Annotations on the Wikimedia Infrastructure

Here we present how two independent infrastructures, Wikimedia and iNaturalist, can be jointly leveraged to improve content on both platforms. iNaturalist.org began as a Master's final project in 2008 and grew to a globally used app to help identify biodiversity. The community behind iNaturalist consists of citizen scientists, who record a species existence through photos or sound recordings. The Wikimedia Foundation provides a spectrum of resources, of which Wikipedia is the most famous sibling. Other siblings we address here are Wikimedia Commons and Wikidata. Commons is the platform where open licensed media can be shared. Wikidata is the linked knowledge graph, where public data can be stored as structured data. Basically, data goes to Wikidata, images and recordings go to Commons and text goes to Wikipedia. Initially, both Commons and Wikidata served primarily the approximately 300 language versions of Wikipedia. However, nowadays both Commons and Wikidata are also being used as is, that is in other contexts than Wikipedia. Although iNaturalist and the Wikimedia family of repositories thrive as independent infrastructures and are thus maintained independently from each other, they can be mutually beneficial to each other. Content created by the iNaturalist community can be very valuable to the Wikimedia community. Firstly, observations in the form of photos and sound recordings can be stored on iNaturalist, with an open and compatible license that can provide valuable illustrations and structured knowledge on biodiversity. Wikipedia articles can be enriched with already approximately 1.2M photos from observations. Furthermore, the assertions made by the iNaturalist community can act as references in various Wikidata claims or Wikipedia articles. In many cases in Wikipedia, we have to rely on personal annotations by the picture taker, who stores it on Wikimedia’s multimedia Commons. iNaturalist provides images of organisms with stronger - peer-reviewed - assertions on the subject in the picture. When a cat is called a cat in Wikipedia or Commons, it is the iNaturalist community that either approves or rejects that claim. In the opposite direction, iNaturalist relies on knowledge described in Wikipedia. It includes Wikipedia articles about taxa on its website. Images can also be uploaded into iNaturalist from Commons. It is therefore possible to add images of museum specimens into iNaturalist to assist with taxon identification. Other citizen science apps do exist. iNaturalist, however, is particularly interesting due to the feature that it allows its users to select from a selection of licenses, of which some are compatible with the licenses upon which content from the Wikimedia family is available. Wikidata uses a CC0 - public - license for its data, Wikipedia is available under a CC-BY-SA and Commons content uses a selection of Creative Commons licenses (Table 1). iNaturalist is also a particular good fit with Wikimedia, because both have a global and multilingual scope. This is a great example of how platforms can support each other's missions by simple policy decisions, such as open licencing, that underpin interoperability.

Wikidata as a linked-data hub for Biodiversity data

Wikidata (http://www.wikidata.org) is the linked database of the Wikimedia Foundation. Like its sister project Wikipedia it is open to humans and machines. Initially primarily intended as a central repository of structured data for the approximately 200 language versions of Wikipedia, Wikidata currently also serves many other use cases. It is an open, Semantic Web-compatible database that anyone can edit. Here, we present the Gene Wiki initiative. In 2008, this project started by creating Wikipedia articles for all human genes (Huss et al. 2008). These articles were enriched with structured information on these genes as tables (called infoboxes). With the onset of Wikidata in 2012, the project diverted its attention from the infoboxes and since we have been enriching Wikidata with structured knowledge from public scientific resources on gene, proteins, diseases and compounds (Burgstaller-Muehlbacher et al. 2016). This structured information is added to Wikidata, while active links to the primary source are maintained. Adding a new resource to Wikidata is a community-driven process that starts with modelling the subjects of the resource under scrutiny. This involves seeking commonalities with similar concepts in Wikidata and, if none are found, new are created. This process mostly happens in a collaboratively-edited document (i.e. GDocs), where different graphical networks are drawn to reflect the data being modelled and its embedding in Wikidata. Once consensus has been reached, the model typically exists in a human-readable document. To allow future validations of these models on existing data, it is converted in a machine-readable Shape Expression (ShEx) (Anonymous 2019, Waagmeester et al. 2017). Shape Expressions schema language can be consumed and produced by humans and machines and is useful in model development, legacy review or as formal documentation. Once a semantic data model (as Shape Expression) is found, i.e. community consensus is reached, a bot is developed to convert the knowledge from the primary source, into the Wikidata model. While Wikidata is linked data (part of the semantic web), many life science resources are not. On the contrary, many distinct file formats or API output formats are used to present life-science knowledge. To convert between these different formats, bots need to be developed that are able to parse the different resources and serialize into wikidata. We have developed a software library in the Python programming language, which we use to build these bots. Once created, these bots run regularly to keep Wikidata up-to-date with knowledge on genes, proteins, diseases and drugs. Having scientific knowledge represented in Wikidata comes with benefits. First, having research data on Wikidata increases its sustainability. When research projects end, their findings now remain on an independently funded infrastructure. Having someone else dealing with an infrastructure for a data commons also relieves the research community of having to do it themselves, leading to more time to focus on doing research As a generic public data commons, Wikidata allows public scrutiny and rapid integration with other domains. Inconsistencies or disagreement between resources become more visible, due to the unified data models and interfaces. The latter we leverage as a feature in our bots. One of our core resources is, for example, the Disease Ontology (Schriml et al. 2018). This ontology on human diseases is continuously updated by its curation team. 2 times per month, updates are then synchronised with Wikidata. If inconsistencies and disagreement with other resources surface, they are logged and shared with the curation team of the Disease Ontology. Hence, we have created a bi-directional update cycle, improving both the Disease Ontology and Wikidata. Although our bots focus on molecular biology, our approaches are generic in onset that we are confident a similar approach can work in biodiversity informatics.

The bibliographic metadata in Wikidata: Wikicite and the «Bibliothecae.it» case study

In recent years, the attention of the librarian world aimed at the progress of Linked Open Data technology and Wikimedia projects has focused on Wikidata, whose infrastructure has proved to be perfectly compatible with Wikicite, an initiative that aims to build a vast database of bibliographic references in linked open data. As part of this initiative, many projects originate from collaborations between the Wikimedia Foundation and libraries: the Library of the University of Salerno also actively participated in the initiative, including the bibliographic metadata of the journal Bibliothecae.it. It is the first italian example of corpus related to a scientific periodical of bibliographicbiblioteconomical scope entirely loaded in Wikidata. The project allows the use of data in any project or program that uses the LOD technology, in order to facilitate the interchange between cultural resources and satisfy the information needs of the user. In the area of bibliography and librarianship, this action represents a new way of creating and sharing bibliographic data, of doing information retrieval and of conducting bibliometric analysis. The positive effects of the project concern the wiki community, which needs reliable and verified sources to base its contents, and the scientific community: by openly publishing the citations sets, an enormous graph of scientific knowledge is opened, encouraging the dissemination, publication and open evaluation of research results.

Review: Wikipedia: The Free Encyclopedia

Wikipedia: The Free Encyclopedia. Wikimedia Foundation. URL: http://wikipedia.org When you type “Why do academics?” as an open question into Google, the autocomplete algorithm offers several suggestions. “Why do academics hate Wikipedia?” was the fifth option in my September 2018 search, nestled between “use macs” and “dress so badly.” (“Write so badly” was first.) Completing the search yields articles such as “Why do teachers hate Wikipedia?” and “Do some academics feel threatened by Wikipedia?” Wikipedia itself offers at least one clue: “citation of Wikipedia in research papers may be considered unacceptable, because Wikipedia is not a reliable source.”1 Wikipedia's status as the world's fifth most visited website should nevertheless give music scholars pause.2 “Aretha Franklin” was the site's most visited page from August 12 to 18, 2018, which should not be surprising given her death on August 16.3 Over a sixty-day period between July 18 and September 16, 2018, page views for the Queen of Soul numbered 7,078,291. In the same period, the 370,362 views for Wolfgang Amadeus Mozart eclipsed J. S. Bach's 204,218 and Duke Ellington's 82,114. Beyonce's page sat at 963,052, while Michael Jackson's totaled 1,685,280.4 According to Taylor & Francis, one of the largest publishers of peer-reviewed academic journals, the unofficial views total for my 2014 article on Ellington in Jazz Perspectives at the time of this review's writing was 167.5 As of October 2018, Wikipedia …

Spacetime Characterization of Real-Time Collaborative Editing

Real-Time Collaborative Editing (RTCE) is a popular way of instrumenting cooperative work on documents, in particular on the Web. Little is known in the literature yet about RTCE usage patterns in the real world. In this paper we study how a popular RTCE editor (Etherpad) is used in the wild, digging into the edit histories of a large collection of documents (about 14 000 pads), retrieved from one of the most popular public instances of the platform, hosted by the Wikimedia Foundation. The pad analysis is supported by a novel conceptual model that allows to label edit operations as "collaborative" or not depending on their distance-in edit position (space), edit time, or spacetime (both)-from edits made by other authors. The model is applied to classify all edits from the pad corpus. Classification results are further used to characterize the collaboration behavior of pad authors. Findings show that: 1) about half of the pads have a single author and hence witnessed no collaboration; 2) collaboration on common document parts happens often, but it happens asynchronously with authors taking turns in editing; and 3) simultaneous editing of common document parts happens very rarely. These findings help in revisiting early RTCE design decisions (e.g., the granularity of conflict management in RTCE protocols) and give insights on how to address novel needs (e.g., end-to-end encryption and offline editing).

Fighting for the Internet: Online Blackout Protests and Internet Legislation in the United States, 1996-2018

Fighting for the Internet: Online Blackout Protests and Internet Legislation in the United States, 1996-2018

The_Tower_of_Babel.jpg: Diversity of Visual Encyclopedic Knowledge Across Wikipedia Language Editions

Across all Wikipedia language editions, millions of images augment text in critical ways. This visual encyclopedic knowledge is an important form of wikiwork for editors, a critical part of reader experience, an emerging resource for machine learning, and a lens into cultural differences. However, Wikipedia research--and cross-language edition Wikipedia research in particular--has thus far been limited to text. In this paper, we assess the diversity of visual encyclopedic knowledge across 25 language editions and compare our findings to those reported for textual content. Unlike text, translation in images is largely unnecessary. Additionally, the Wikimedia Foundation, through the Wikipedia Commons, has taken steps to simplify cross-language image sharing. While we may expect that these factors would reduce image diversity, we find that cross-language image diversity rivals, and often exceeds, that found in text. We find that diversity varies between language pairs and content types, but that many images are unique to different language editions. Our findings have implications for readers (in what imagery they see), for editors (in deciding what images to use), for researchers (who study cultural variations), and for machine learning developers (who use Wikipedia for training models).

Brief of Amici Curiae Electronic Frontier Foundation - FX Networks, LLC v. Olivia de Havilland

Together with the Wikimedia Foundation and the Organization for Transformative Works, EFF filed this amicus brief in FX Networks v. Olivia de Havilland. The brief argues that the First Amendment should shield creative expression like the Feud TV show from right of publicity claims.

The Monkey Selfie case and the concept of authorship: an EU perspective

... The dispute between, initially, the Wikimedia Foundation and UK wildlife photographer David Slater and, subsequently, between the latter and People for the Ethical Treatment of Animals (PETA) over copyright ownership of the (now infamous) self-portrait photographs—selfies—taken in Indonesia by a Celebes crested macaque named Naruto (also known as the ‘Monkey Selfies’) has attracted widespread attention, both among lawyers and the general public. In 2014 the Wikimedia Foundation rejected Slater’s takedown request to remove from a Wikipedia page one of the photographs taken by Naruto. The Foundation did so on grounds that no copyright could subsist in a work created by an animal. It also concluded that the argument by Slater that he had befriended a group of macaques and set up the photographic equipment, so that they would use it and possibly take pictures (with him editing the resulting photographs), held no value.1

Stages of motivation for contributing user-generated content: A theory and empirical test

Motives for contributing content are argued to differ for participants at different stages.A stage theory that distinguishes three sets of motivations for participation is proposed.The theory is tested with a data set from the Wikimedia Editor Survey.Results support the premise that motives are not unitary but differ by stage. User-generated content (UGC) projects involve large numbers of mostly unpaid contributors collaborating to create content. Motivation for such contributions has been an active area of research. In prior research, motivation for contribution to UGC has been considered a single, static and individual phenomenon. In this paper, we argue that it is instead three separate but interrelated phenomena. Using the theory of helping behaviour as a framework and integrating social movement theory, we propose a stage theory that distinguishes three separate sets (initial, sustained and meta) of motivations for participation in UGC. We test this theory using a data set from a Wikimedia Editor Survey (Wikimedia Foundation, 2011). The results suggest several opportunities for further refinement of the theory but provide support for the main hypothesis, that different stages of contribution have distinct motives. The theory has implications for both researchers and practitioners who manage UGC projects.

Can the U.S. government legally monitor private communications? If so, given the U.S.'s significant protection of privacy rights, what government cannot?

Wikimedia Foundation v. NSA/CSS is the most important case ever regarding whether a government can monitor private communications. This case's discussion could not be any more timely for public affairs purposes. Given continuing terrorist attacks and concomitant calls for private industries, such as Wikimedia Foundation, to do more to stop the spread of violence and rhetoric on the Internet, there has never been a more important time to consider what limits should be placed on government's access to personal data in the quest to prevent terrorism. Here, the district court referred to the plaintiffs' injuries from NSA upstream surveillance as speculative, denying them Article III standing. The Fourth Circuit reinstated Wikimedia Foundation as a plaintiff, finding sufficient allegations for it to survive a facial challenge to Article III standing based on the Wikimedia Allegation. However, the other plaintiffs, relying on the Dragnet Allegation, were properly dismissed based on lacking standing. Until more details of upstream surveillance become public, suing under a dragnet theory should prove difficult. In fact, lobbying the legislative and executive branches to enact changes could be more effective and efficient than relying on the courts.

3000 Hits Lesson Overview

My favorite lesson is a statistics exploration using data from Major League Baseball's (MLB) 3000-Hits Club. Students in my introductorystatistics course analyze two 1-variable data sets—career batting average and total career hits—that have significant differences in variability and distribution (Wikimedia Foundation 2017a).

Expanding the sum of all human knowledge: Wikipedia, translation and linguistic justice

ABSTRACTThe Wikimedia Foundation is arguably the quintessential example of a socially responsible organisation: not-for-profit, dedicated to the growth, development and distribution of free, multilingual content, the Foundation operates openly editable projects like Wikipedia, which now has anywhere from one to several million articles available in more than 280 languages. Wikimedia – the organisation that hosts and operates Wikipedia – does not appear to have an explicit translation policy. This paper therefore begins by assessing the organisation’s Language Proposal Policy and Wikipedia’s translation guidelines. Then, drawing on statistics from the Content Translation tool recently developed by Wikipedia to encourage translation within the various language versions, this paper applies the concept of linguistic justice to help determine how any future translation policies might achieve a better balance between fairness and efficiency, arguing that a translation policy can be both fair and efficient, while still conforming to the ‘official multilingualism’ model that seems to be endorsed by the Wikimedia Foundation.

50/50 Norm in Massive Online Public Good: The Case of Wikipedia

This paper shows the existence of a strong social norm in a massive online public good community, demonstrated by the choice of the equal split in the Dictator Game (DG). With the help of the French Wikimedia Foundation, we questioned a large sample of Wikipedia users and contributors on their practices, and then asked them to play the DG. The results are statistically significant and show how people respect (or not) social norms. They also show that both contributors and non-contributors adhere to a norm that it is correlated with their “patronage” of Wikipedia. Regular, long-term users, who declare a strong attachment to the platform, are more likely to choose the 50/50 split in the DG. The method of instrumental variables was used to identify a causal relationship between the equal split and involvement in Wikipedia. This did not invalidate the hypothesis that choosing an equal split in the DG is driven by the level of involvement in Wikipedia.

Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity

In this paper, we study the network of global interconnections between language communities, based on shared co-editing interests of Wikipedia editors, and show that although English is discussed as a potential lingua franca of the digital space, its domination disappears in the network of co-editing similarities, and instead local connections come to the forefront. Out of the hypotheses we explored, bilingualism, linguistic similarity of languages, and shared religion provide the best explanations for the similarity of interests between cultural communities. Population attraction and geographical proximity are also significant, but much weaker factors bringing communities together. In addition, we present an approach that allows for extracting significant cultural borders from editing activity of Wikipedia users, and comparing a set of hypotheses about the social mechanisms generating these borders. Our study sheds light on how culture is reflected in the collective process of archiving knowledge on Wikipedia, and demonstrates that cross-lingual interconnections on Wikipedia are not dominated by one powerful language. Our findings also raise some important policy questions for the Wikimedia Foundation.

Monetary Materialities of Peer-Produced Knowledge: The Case of Wikipedia and Its Tensions with Paid Labour

This article contributes to the debate on the possibilities and limits of expanding the sphere of peer production within and beyond capitalism. As a case in point, it discusses the explicit and tacit monetary dependencies of Wikipedia, which are not only due to the need to sustain the technological structures that render the collaboration possible, but also about the sustenance of the peer producers themselves. In Wikipedia, the “bright line” principle for avoiding a conflict of interest has been that no one should be paid for directly editing an article. By examining the case of Wiki-PR, a consultant firm allegedly involved in helping more than 12,000 clients to edit Wikipedia articles until 2014, the goal of the analysis is to shed light on the paradoxical situation where the institution for supporting the peer production (Wikimedia Foundation) found itself taking a more strict perspective against commercial alliances than the unpaid community editors.

Shifting from classic learning environment to digital learning environment in Arab culture

Social media discussion forums, blogs, wikis and three-dimensional virtual worlds are the current new techniques for learning. External social media sites, including Facebook (Facebook, Inc., Menlo Park, CA, USA), YouTube (YouTube, LLC, San Bruno, CA, USA), Wikipedia (Wikimedia Foundation, Inc., San Francisco, CA, USA), Flickr (Yahoo Inc., Sunnyvale, CA, USA), Twitter (Twitter, Inc., San Francisco, CA, USA), LinkedIn (LinkedIn, Mountain View, CA, USA) and Second Life (Linden Lab, San Francisco, CA, USA), have been considered as very useful learning and teaching tools in recent years. This provides a chance for student-centred and social constructivist pedagogies in an online environment, and enables students to easily contribute and collaborate to e-learning environments. The influences of social media have reached Arab culture and are quickly spreading. This study highlights the use of these media for self-directed learning (SDL) purposes among medical students. This is a cross-sectional study and the questionnaire was constructed using the Delphi technique at Google (Google Inc., Mountain View, CA, USA) documents and was uploaded to different social media sites. The questionnaire comprised 28 questions, which included information relating to demographic features and utilization of social media during SDL time. The data were entered into Statistical Product and Service Solutions (SPSS) version 21 (IBM Corporation, Armonk, NY, USA) and Pearson chi-squared tests were applied for comparison. We received a total of 103 responses in 1 week, 52 of which (50.5%) were from men. The greatest proportion of responses came from students in their fifth year (35%), followed by those in their fourth year and first year (18.4%). Most students (63%) scored a grade-point average of 4–5 points. Out of the total, only eight students (7.8%) responded that they do not use social network sites at all. Almost one-third of responders (33%) utilized social networks for SDL for 2–3 hours daily, and 84 responders (81%) declared that these social media sites are helpful during preparation for their examinations. Age, sex and academic year have a significant association ( P <0.05) with the use of social media. In conclusion, it is reported that the culture of using of social media is increasing in Arab society; however, we need to provide the guidance for its proper utilization for SDL purposes.