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COMP2782 Social And Information Networks

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COMP2782 Social And Information Networks

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COMP2782 Social And Information Networks

0 Download6 Pages / 1,391 Words

Course Code: COMP2782
University: Flinders University

MyAssignmentHelp.com is not sponsored or endorsed by this college or university

Country: Australia

Questions:

1. Demonstrate data scraping of a social network of choice.
2. Develop technical documentation, including the development of the code & detailing the results.
3. Provide a report on the findings, that includes research into the ethics & security ramifications of your ability to data scrape, and any suggested mitigations

Answer:
Introduction
For one reason or the other we are all consumers of information from the web. Social media sites are commonly used to get a feel of what a person is like. By seeing the kind of posts a person updates as well the kind of posts the person likes on Facebook, Twitter, Linkedin, Instagram etc., it is very easy to come up with the persons taste and preferences. Many people may not think this information is important but that is probably because they have not yet been faced with an advertising agenda. There are techniques that have been developed using computer programs to make it easy to obtain data from social media pages as well as web pages. This practice is called web scraping. Most of the times data is obtained from web pages and social media sites, the owners do not have an idea that their data is being extracted. According to the law, it is illegal to obtain someone’s information from whatever platform without their knowledge. Facebook and Twitter have come up with a mitigation to this issue. They have the HTML content specific to a particular page hidden whenever anyone tries to view source page. Anyone who wants to scrap data from their webpages then has to create a developers account corresponding to their Twitter or Facebook accounts. With a developers account, one is able to use APIs to scrap data from Twitter or Facebook. Nevertheless, this does not stop anyone from web scraping. Anyone can still create a developers account and be able to do web scraping. It is therefore not an issue of what the law says but rather an issue of ethics.  
Data Scrapping
Data scraping is a process that involves using a computer software to extract data from social media sites and the web at large. Normally, anyone can view the page source of any web page by right clicking on the page and selecting view page source. Viewing the page source gives the HTML contents as well as CSS for the particular page. The content of the page source is normally messy and may have very little meaning to a non-programmer. On the other hand, this is exactly what a programmer needs to start with and narrow down to the particular details being examined about some target users.
After viewing page source, one can user a computer software like python to write a program that filters the data needed and even does some analysis on the data. For example, one can access a web page and extract user’s emails, contacts  
The key aims of data scraping are data analysis, customer information acquisition, price comparison and other competitor data, data visualization, market research, academic research, product catalog scraping, and more. 
Ethics, Privacy and Security Ramifications
Is Data Scraping Ethical?
Data scraping being ethical or unethical is a question of the point from which you look at it. Mostly we can only answer this question depending on the use. For one, the study participants have the right to informed consent. The results of the analysis also have to be given out anonymously, that is, the recipients of the analysis should not be able to point which data was collected from which individual. This is normally the disclaimer in data collection of any other form, e.g. observation, questionnaires, etc. Therefore if data scraping is done with the study participants knowledge and the results of analysis reported anonymously, then that is just a research process like the use of surveys, questionnaires etc. It thus cannot be termed as unethical. Actually, this kind of data proves to be of much help especially in customer information acquisition, market research, academic research and more. It is data that was given out by the participant without any expectations and thus sincere and exactly what is needed in any form of research.
On the other hand, any study on participants without their knowledge is unethical. So is the case when the results of the study disclose the subjects of study.
But the big question is, “How will anyone get hold of the vast population involved in social media?” It is very unlikely to get hold of study participants in any data scraping that involves social media especially Facebook and Twitter. This has led to the owners of these social media sites taking it upon themselves to restrict on access to individuals data through web scraping.
Privacy
Privacy is clearly not a great issue in this case because anything put out in social media and websites is not private. Since data scraping only extracts data that has already been posted by the study subject, there are no serious issues with privacy. Either way, when direct competitors use web scraping to extract data for price comparison and other competitor data, privacy may be invaded to some extend but either way, privacy is not a really serious issue.
Security Ramifications
Since it is not easy to get hold of social media sites contributors to ask for their consent to extract their data, the issue of security has escalated to the administrators and owners of the social sites. The current situation is that you cannot access any Facebook or Twitter user content by web scrapping without authorizations. The site owners can grant authorization to those who want to do data scraping on their sites by having them answer some policy questions and agree to some terms and conditions. After review, if the reasons for data scraping are good enough, the site administrators can grant access to the sites as a developer. When one has access to a site as a developer, they can freely do data mining and analysis.
Conclusion
Data scraping in itself cannot be classified as ethical or unethical, but the use can. Data scraping for the reasons of analysis in scientific research with the study subjects consent is definitely ethical. Data scraping without the study subjects consent for whatever use is unethical. Either way, data scraping is a great innovation that has helped so much in market research and other studies especially in governments and academics. It is therefore the future in researches and the only thing that needs to be done is improve on the security so that not just anyone that can get access to HTML content for data scraping.  
References
Mitchell, R., 2018. Web Scraping with Python: Collecting More Data from the Modern Web. ” O’Reilly Media, Inc.”.
Munzert, S., Rubba, C., Meißner, P. and Nyhuis, D., 2014. Automated data collection with R: A practical guide to web scraping and text mining. John Wiley & Sons.
Vargiu, E. and Urru, M., 2012. Exploiting web scraping in a collaborative filtering-based approach to web advertising. Artificial Intelligence Research, 2(1), p.44.
Haddaway, N.R., 2015. The use of web-scraping software in searching for grey literature. Grey J, 11(3), pp.186-90.
Boeing, G. and Waddell, P., 2017. New insights into rental housing markets across the United States: Web scraping and analyzing craigslist rental listings. Journal of Planning Education and Research, 37(4), pp.457-476.
Ferrara, E., De Meo, P., Fiumara, G. and Baumgartner, R., 2014. Web data extraction, applications and techniques: A survey. Knowledge-based systems, 70, pp.301-323.
Salerno, J.J. and Boulware, D.M., US Air Force, 2006. Method and apparatus for improved web scraping. U.S. Patent 7,072,890.
Kokkoras, F., Ntonas, K. and Bassiliades, N., 2013, September. DEiXTo: a web data extraction suite. In Proceedings of the 6th Balkan Conference in Informatics (pp. 9-12). ACM.
Baumgartner, R., Gottlob, G. and Herzog, M., 2009. Scalable web data extraction for online market intelligence. Proceedings of the VLDB Endowment, 2(2), pp.1512-1523.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big data to big impact. MIS quarterly, pp.1165-1188.
Srivastava, J., Cooley, R., Deshpande, M. and Tan, P.N., 2000. Web usage mining: Discovery and applications of usage patterns from web data. Acm Sigkdd Explorations Newsletter, 1(2), pp.12-23.
Baumgartner, R., Frölich, O., Gottlob, G., Harz, P., Herzog, M., Lehmann, P. and Wien, T., 2005. Web data extraction for business intelligence: the lixto approach (pp. 30-47).
Zimmer, M., 2010. “But the data is already public”: on the ethics of research in Facebook. Ethics and information technology, 12(4), pp.313-325.
Markham, A., Buchanan, E. and AoIR Ethics Working Committee, 2012. Ethical decision-making and Internet research: Version 2.0. Association of Internet Researchers.

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