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Evaluate an Article: Information Privacy

Which article are you evaluating?

Information privacy

Why you have chosen this article to evaluate?

I chose this article as it was assigned by the executive director of my research group. It matters because it describes the privacy of individual information and the different domains in which the privacy of data is or is not upheld. This is important to our research, given the focus of the lab being on the dissemination of knowledge about privacy. My preliminary impression of it is that it is a good starting article for many of the domains. It provides many links to the legal aspect of information privacy, which is appreciated. I do wish that some of the domains were more developed and also felt that certain domains were missing, such as more information on cable tv, streaming services and other types of information.

Evaluate the article

The lead section provided a decent overview about what information privacy is. The lead sentence of the article concisely describes the connection between data, privacy and the law and mentions the fields to which the topic is most pertinent. It does not mention the different domains of data that are relevant, however information about the privacy of different types of information are located in the article. The lead is quite short and to the point.

The article's content is relevant to the topic, given its focuses on the privacy of different types of data, the laws and regulations related to data privacy and various strategies taken by nations and developers of technical systems to uphold information privacy. No claims in the article seem to be heavily biased towards a particular position. The descriptions of data privacy in the different domains of data have a neutral point of view and convey relevant information with appropriate citations. The legal discussion similarly maintains an unbiased perspective. Both the portions of the US Safe Harbor Program and the portions on the strategies taken by developers of technical systems simply disseminate the information of what occurred. Given the mentions of the laws of different nations and the links provided to such regulations, no specific country is focused on. The article in no way tries to persuade the reader in favor of one position of another.

All the facts in the article seem to be backed up by a reliable secondary source of information, given the usage of books, international newspapers, peer-reviewed research papers and legal regulations. Most of the sources used are relatively current, having been used in the past 10 to 15 years. There are be better and more current sources available that would help build the article into a more informative page, such as more information on social media privacy, the privacy of information in internet-of-things devices and information collected by public health departments during the Covid-19 Pandemic. After having checked a few links, they do work and do link to the appropriate sources.

The article is well-written and easy to read. I did not find any grammatical errors in the article. I do think that the article is also split up appropriately.

There are no images included in this article.

The talk page provides different edits suggested by individuals, such as where information is lacking or where more could be added. For example, one user mentions that the internet section can be improved with more information about recent privacy violations that occurred through social media. Others suggest different perspectives that can be added to the article and new sections that can be created for the article, such as the addition of information on search engine data privacy. This article is part of the Computing, Internet and Mass surveillance WikiProjects. This article was similar to how such topics were discussed in class. The article has a C-Class score.

Overall, the article is well-written, concise and provides a decent amount of information that is relevant to the topic. The article's strengths are in the unbiased perspective and the breadth of information provided. The article could be improved by adding newer information that would strengthen already existing information. I would assess that this article is slightly under-developed and has room for improvement.

Evaluate an Article: Surveillance Capitalism

Which article are you evaluating?

Surveillance capitalism

Why you have chosen this article to evaluate?

I chose this article given my interest in the topics of social media privacy and of machine learning models based on personal data. It matters given how the privacy of individual data is compromised due to the purpose of profit-making. My preliminary impression of it is that it is an eye-opening article that describes what data can be used to influence and control the behavior of individuals and why such models would be created and used so widely.

Evaluate the article

The lead section provided a decent overview about what surveillance capitalism is. The lead sentence of the article concisely describes a profit-driven system based on the commercialization of data. The introduction as a whole goes into much detail about the specific service that brought about surveillance capitalism, the pros and cons of data collection, the focus of capitalism to utilize data to make more profit and the privacy leakage that occurs as a result.

The article's content is relevant to the topic, starting off with a background on the topic from Shoshana Zuboff, who introduced the term, going into her theory and ending with the response of various governments, organizations and companies to deal with the issues brought up by her theory. The content is up-to-date, given the discussion of surveillance capitalism around current events, such as the Facebook Cambdrige Analytica Scandal and discussion of the Covid-19 Pandemic. No claims in the article seem to be biased towards one viewpoint or another, given the discussion of the both the theory and the response to the theory. The article discusses a topic that is not widely discussed, given the prevalence of companies today to employ the tenets of surveillance capitalism in their work.

All the facts in the article seem to be backed up by a reliable secondary source of information, given the usage of books, international newspapers, peer-reviewed research papers and legal regulations. Most of the sources used are relatively current, having been used in the past 10 years. There are be better and more current sources available that would help build the article into a more informative page, such as more information on new practices by companies to avoid surveillance capitalism or organizations spreading awareness about surveillance capitalism, such as the Center for Humane Technology. After having checked a few links, they do work and do link to the appropriate sources.

The article is well-written and easy to read. I did not find any grammatical errors in the article. I do think that the article is also split up appropriately.

There are no images included in this article.

The talk page provides different edits suggested by individuals, such as information that is related to surveillance capitalism, comparisons to other existing articles on Wikipedia and questions about the topic itself. Others suggest that this article is biased against the opposing view of surveillance capitalism. This article is of interest to the Surveillance, Business, Economics, Politics, Law, International Relations/Law, Globalization, Sociology, Computing, History, Internet, Internet Culture, Futures Studies, Google and Capitalism WikiProjects.

Overall, the article is well-written, concise and provides a good amount of information that is relevant to the topic. I think that more information from companies that that monetize data would be helpful. Thus, I would suggest that the article is under-developed and has room for improvement.

Bing Liu: What I plan to contribute

On the Bing Liu article, I plan to add more detailed information about Bing Liu's research as published in peer-reviewed academic journals. Currently, there are only a few sentences that do not go into much detail about what his research is actually about. I also intend on updating the list of articles to include all the peer-reviewed articles that Bing Liu has worked on.

Improving Existing Article: Bing Liu

Notes for Improvement/What is missing?

• Adding peer-reviewed article list to Wikipedia page.
• Posting more detailed information about Bing Liu's contributions on the topics he has specialized in such as sentiment analysis.

Possible Additional Sections

• Peer-reviewed article list.
• Detailed contributions on sentiment analysis/opinion mining.

Related Wikipedia Articles to look at

• Sentiment analysis
• Associative classifier
• Machine learning
• Natural language processing

Improving Existing Article: Bing Liu - Article Draft

Detailed Information to be added to Academic Research Portion

More info on association rules for prediction

Association rule-based classification takes into account the relationships between each item in a dataset and the class into which one is trying to classify that item. The basis is that there are two classes, a positive class and a negative class, into which one classifies items. Some classification algorithms only check if a case/item is in the positive class, without understanding how much exactly the probability of it being in that class is. Liu and his collaborators described a new association rule-based classification algorithm that takes into account the relationship between items and the positive and negative classes. Each item is given a probability or scoring of being in the positive class or the negative class. It then ranks the items as per which ones would be most likely to be in the positive class.

More info on sentiment analysis

In a paper that Liu collaborated on, "Opinion Word Expansion and Target Extraction through Double Propagation", Qiu, Liu, Bu and Chen studied the relationship between opinion lexicons and opinion targets. Opinion lexicons are word sets and opinion targets are topics on which there is an opinion. The authors discuss how their algorithm uses a limited opinion word set with the topic and through double propagation, one is able to form a more detailed opinion word set on a set of sentences. Double propagation is the back and forth functional process between the word set and topic as the word set updates itself. Some algorithms require set rules and thus are limited in what they can actually do and in what service they provide through updated opinion lists. Their algorithm only requires an initial word set, which is updated through finding relations between the words in the set and the target word or vice versa. The algorithm is done on a word population such as a set of sentences or a paragraph.

Articles(Peer-reviewed Article List)

• Liu, Bing, Yiming Ma, Ching Kian Wong, and Philip S. Yu. 2003. “Scoring the Data Using Association Rules.” Applied Intelligence 18(2):119–35.
• Qiu, Guang, Bing Liu, Jiajun Bu, and Chun Chen. 2011. “Opinion Word Expansion and Target Extraction through Double Propagation.” Computational Linguistics 37(1):9–27.

• Wu, Xindong et al. 2007. “Top 10 Algorithms in Data Mining.” Knowledge and Information Systems 14(1):1–37.
• Liu, Bing. 1995. “A Unified Framework for Consistency Check.” International Journal of Intelligent Systems 10(8):691–713.
• Zhang, Lei, Shuai Wang, and Bing Liu. 2018. “Deep Learning for Sentiment Analysis: A Survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4).
• Wang, Guan, Sihong Xie, Bing Liu, and Philip S. Yu. 2012. “Identify Online Store Review Spammers via Social Review Graph.” ACM Transactions on Intelligent Systems and Technology 3(4):1–21.
• Yu, Zeng et al. 2019. “Reconstruction of Hidden Representation for Robust Feature Extraction.” ACM Transactions on Intelligent Systems and Technology 10(2):1–24.
• Wang, Jing, Clement T. Yu, Philip S. Yu, Bing Liu, and Weiyi Meng. 2015. “Diversionary Comments under Blog Posts.” ACM Transactions on the Web 9(4):1–34.
• Bing Liu, Wynne Hsu, Lai-Fun Mun, and Hing-Yan Lee. 1999. “Finding Interesting Patterns Using User Expectations.” IEEE Transactions on Knowledge and Data Engineering 11(6):817–32.
• Yanhong Zhai and Bing Liu. 2006. “Structured Data Extraction from the Web Based on Partial Tree Alignment.” IEEE Transactions on Knowledge and Data Engineering 18(12):1614–28.
• Yu, Huilin, Tieyun Qian, Yile Liang, and Bing Liu. 2020. “AGTR: Adversarial Generation of Target Review for Rating Prediction.” Data Science and Engineering 5(4):346–59.
• Bing Liu. 1997. “Route Finding by Using Knowledge about the Road Network.” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 27(4):436–48.
• Liu, Bing. 1993. “Problem Acquisition in Scheduling Domains.” Expert Systems with Applications 6(3):257–65.
• Liu, Bing. 1993. “Knowledge-Based Factory Scheduling: Resource Allocation and Constraint Satisfaction.” Expert Systems with Applications 6(3):349–59.
• Bing Liu, R. Grossman, and Yanhong Zhai. 2004. “Mining Web Pages for Data Records.” IEEE Intelligent Systems 19(06):49–55.
• Bing Liu, Wynne Hsu, Shu Chen, and Yiming Ma. 2000. “Analyzing the Subjective Interestingness of Association Rules.” IEEE Intelligent Systems 15(5):47–55.
• Liu, Bing and Alexander Tuzhilin. 2008. “Managing Large Collections of Data Mining Models.” Communications of the ACM 51(2):85–89.
• Liu, Qian, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2016. “Automated Rule Selection for Opinion Target Extraction.” Knowledge-Based Systems 104:74–88.
• Liu, Bing. 2017. “Lifelong Machine Learning: a Paradigm for Continuous Learning.” Frontiers of Computer Science 11(3):359–61.
• Poria, Soujanya, Ong Yew Soon, Bing Liu, and Lidong Bing. 2020. “Affect Recognition for Multimodal Natural Language Processing.” Cognitive Computation 13(2):229–30.
• Qian, Yuhua, Hang Xu, Jiye Liang, Bing Liu, and Jieting Wang. 2015. “Fusing Monotonic Decision Trees.” IEEE Transactions on Knowledge and Data Engineering 27(10):2717–28.
• Wang, Hao, Yan Yang, Bing Liu, and Hamido Fujita. 2019. “A Study of Graph-Based System for Multi-View Clustering.” Knowledge-Based Systems 163:1009–19.
• Li, Huayi, Bing Liu, Arjun Mukherjee, and Jidong Shao. 2014. “Spotting Fake Reviews Using Positive-Unlabeled Learning.” Computación y Sistemas 18(3).
• Zhai, Zhongwu, Bing Liu, Jingyuan Wang, Hua Xu, and Peifa Jia. 2012. “Product Feature Grouping for Opinion Mining.” IEEE Intelligent Systems 27(4):37–44.
• Apte, Chidanand, Bing Liu, Edwin P. Pednault, and Padhraic Smyth. 2002. “Business Applications of Data Mining.” Communications of the ACM 45(8):49–53.
• Li, Yanni et al. 2020. “ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering.” IEEE Transactions on Knowledge and Data Engineering 1–1.
• Robert Grossman, Pavan Kasturi, Donald Hamelberg, and Bing Liu. 2004. "An Empirical Study of the Universal Chemical Key Algorithm for Assigning Unique Keys to Chemical Compounds." Journal of Bioinformatics and Computational Biology 02(01):155–71.
• Liu, Bing et al. 1994. “Finding the Shortest Route Using Cases, Knowledge, and Djikstra's Algorithm.” IEEE Expert 9(5):7–11.
• Liu, Bing. 1994. "Specific Constraint Handling in Constraint Satisfaction Problems.” International Journal on Artificial Intelligence Tools 03(01):79–96.