2017 |
Reips, Ulf; Garaizar, Pablo Social Lab: An “Open Source Facebook" Book Chapter Sloan, In Quan-Haase & A L (Ed.): Handbook of Social Media Research Methods, pp. 475-485, London: Sage, 2017. Abstract | Links | BibTeX | Tags: big data, Facebook, Internet science, Internet-based research, iscience, open source, social bots, Social Lab, Social media, social networks @inbook{Reips2017, title = {Social Lab: An “Open Source Facebook"}, author = {Ulf Reips and Pablo Garaizar}, editor = {In A. Quan-Haase & L. Sloan}, url = {http://home/learninglabdeust/public_html.uni-konstanz.de/iscience/reips/pubs/papers/chapters/2016ReipsGaraizar.pdf}, year = {2017}, date = {2017-05-24}, booktitle = {Handbook of Social Media Research Methods}, pages = {475-485}, publisher = {London: Sage}, abstract = {The overlap between our every day activities and our behaviours on the Internet is ever increasing. With the advent of social media the social and behavioural sciences are faced with new opportunities and challenges for research into social behaviour. The vast majority of social media are owned by private companies. Despite public application programming interfaces (APIs) being offered by some of these social media, research in proprietary networks is severely limited. Considering the limitations to social media research, we have developed Social Lab, an open source clone of Facebook with most of its features (messaging, sharing, befriending, wall posts, pictures, searching, profiles, privacy settings, etc.). In addition, Social Lab enables researchers to create “social bots” – automated programmable profiles controlled through simple scripts – to facilitate the study of social phenomena. In the present chapter we introduce Social Lab using an example around privacy management in social media, show how to configure social bots in Social Lab, and explain how it can be used in research. The source code of Social Lab is freely available to the scientific community, so any research group can have its own Social Lab to conduct their Internetbased research.}, keywords = {big data, Facebook, Internet science, Internet-based research, iscience, open source, social bots, Social Lab, Social media, social networks}, pubstate = {published}, tppubtype = {inbook} } The overlap between our every day activities and our behaviours on the Internet is ever increasing. With the advent of social media the social and behavioural sciences are faced with new opportunities and challenges for research into social behaviour. The vast majority of social media are owned by private companies. Despite public application programming interfaces (APIs) being offered by some of these social media, research in proprietary networks is severely limited. Considering the limitations to social media research, we have developed Social Lab, an open source clone of Facebook with most of its features (messaging, sharing, befriending, wall posts, pictures, searching, profiles, privacy settings, etc.). In addition, Social Lab enables researchers to create “social bots” – automated programmable profiles controlled through simple scripts – to facilitate the study of social phenomena. In the present chapter we introduce Social Lab using an example around privacy management in social media, show how to configure social bots in Social Lab, and explain how it can be used in research. The source code of Social Lab is freely available to the scientific community, so any research group can have its own Social Lab to conduct their Internetbased research. |
2014 |
Orduña, Pablo; Almeida, Aitor; Ros, Salvador; Garcia-Zubia, Javier; Lopez-de-Ipiña, Diego Leveraging Non-explicit Social Communities for Learning Analytics in Mobile Remote Laboratories Journal Article Journal of universal computer science, 20 (15), 2014. Abstract | Links | BibTeX | Tags: data mining, learning analytics, Remote laboratories, social networks @article{Orduña2014, title = {Leveraging Non-explicit Social Communities for Learning Analytics in Mobile Remote Laboratories}, author = {Pablo Orduña and Aitor Almeida and Salvador Ros and Javier Garcia-Zubia and Diego Lopez-de-Ipiña}, url = {http://home/learninglabdeust/public_html.jucs.org/jucs_20_15/leveraging_non_explicit_social/jucs_20_15_2043_2053_orduna.pdf}, doi = {10.3217/jucs-020-15-2043}, year = {2014}, date = {2014-01-01}, journal = {Journal of universal computer science}, volume = {20}, number = {15}, abstract = {When performing analytics on educational datasets, the best scenario is where the dataset was designed to be analyzed. However, this is often not the case and the data extraction becomes more complicated. This contribution is focused on extracting social networks from a dataset which was not adapted for this type of extraction and where there was no relation among students: a set of remote laboratories where students individually test their experiments by submitting their data to a real remote device. By checking which files are shared among students and submitted individually by them, it is possible to know who is sharing how many files with who, automatically extracting what students are bigger sources. While it is impossible to extract the full real social network of these students, all the edges found are clearly part of it. These relations can indeed be used as a new input for performing the analytics on the dataset. }, keywords = {data mining, learning analytics, Remote laboratories, social networks}, pubstate = {published}, tppubtype = {article} } When performing analytics on educational datasets, the best scenario is where the dataset was designed to be analyzed. However, this is often not the case and the data extraction becomes more complicated. This contribution is focused on extracting social networks from a dataset which was not adapted for this type of extraction and where there was no relation among students: a set of remote laboratories where students individually test their experiments by submitting their data to a real remote device. By checking which files are shared among students and submitted individually by them, it is possible to know who is sharing how many files with who, automatically extracting what students are bigger sources. While it is impossible to extract the full real social network of these students, all the edges found are clearly part of it. These relations can indeed be used as a new input for performing the analytics on the dataset. |