Design Project Preliminary Bibliography (1)
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Jan 9, 2024
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1
Design Project Preliminary Bibliography
Question #1:
How can we teach people how to use the Internet positively and show the impact
of their use of Social Media?
There are numerous ways to teach this to people where we can use the AI-Sentiment Analyzer to
minimize toxic situations and directly work with professionals to create tools that enable
positivity. Where in turn, users are more conscious of how social media can negatively impact
someone and be less likely to engage in online toxicity.
Question #2:
How can we have the AI-Sentiment Analyzer accurately pick up the nuances of
online behaviors as well as creating a product that gives feedback in real time?
IT can be done through having the final product have many prototype stages and gain a wider
team so they can pick up on all the various online behaviors. Knowing these online behaviors
can also predict how that might develop in the future. As well this meaning that accurately
knows their users and the psyche of their online behaviors.
2
Annotated Bibliography
Anjum, A., & Katarya, R. (2023). Hate speech, toxicity detection in online social media: a
recent survey of state of the art and opportunities.
International Journal of Information Security
.
https://doi.org/10.1007/s10207-023-00755-2
Both authors are faculty at the Delhi Technological University who are a part of the Big Data
Analytics and Web Intelligence Laboratory. This source is peer reviewed but it is not a research
source. It is still helpful as it analyzes different studies and explains how different social media
applications affect users. In this source, it talks about a web browser plugin called Hate Speech
Blookers where it determines if a user said something considered as hate speech and then in turn
flags or blocks the user. This is helpful as using this plugin could be a part of the AI-Sentiment
Analyzer.
Barai, M. D., & Sanyal, S. (2021). Comparative Study on Lexicon-based sentiment analysers
over Negative sentiment. (n.d.).
International Journal of Electrical, Electronics and Computers.
https://doi.org/10.22161/ijeec.66.1
This source says it is peer-reviewed but the Rutgers database says otherwise. The Rutgers
database did not include the authors. But the authors themselves are reputable as they are a part
of the Samsung Research Institute. While it is not a research source it is still helpful with their
two sentiment analyzers, VADER and TextBlob. The source also goes into a Lexicon based and a
Machine Learning approach. This helps for answering the questions as in reference to AI, by
using the sentiment analyzers helps pinpoint how users use negative sentiment online platforms.
As well as ranking how much negative sentiment a sentence has.
Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies
between expressed privacy concerns and actual online behavior – A systematic literature review.
Telematics and Informatics, 34(7)
, 1038–1058.
https://doi.org/10.1016/j.tele.2017.04.013
Barth and de Jong are faculty at the Behavioural, Management and Social Sciences at the
University of Twente. While Barth is also part of the Faculty of Electrical Engineering,
Mathematics, and Computer Science at that same university. The article is peer reviewed but not
a research source, it is still helpful. It delves into how users say they want to be protected online
but do not do that much for their data to be protected. It shows the moral essence of online
behaviors where people spread Internet toxicity but do not want that for themselves. Since it
talks about how users understand the privacy paradox which could aid the answer to the question
on how people understand the consequences of using social media negatively.
3
Biddix, J. P., Chung, C. J., & Park, H. W. (2011). Convenience or credibility? A study of college
student online research behaviors.
The Internet and Higher Education, 14(3)
, 175–182.
https://doi.org/10.1016/j.iheduc.2011.01.003
Biddix is a part of the Department of Curriculum, Leadership, and Technology at Valdosta State
University. Chung is a part of the Department of Communication at the University of Buffalo
while Park is part of the Department of Media and Communication at YeungNam University.
Seeing as all three authors are from different places, it gives the source more of a wider
perspective. This source is peer reviewed but not a research source as it uses other sources. It is
still helpful as it talks about the online behaviors of college students. The source mentions using
semantic analysis in respect to social media analysis. It indicates how college students do not
delve further when it comes to research, they use what they see first as their first option for their
bias. This is helpful because for determining how we use AI to determine online behaviors,
observing what users search and correlate to what is the most frequently used searches can also
show which biases are “promoted”.
Gandomi, A. H., Chen, F., & Abualigah, L. (2022). Machine Learning Technologies for Big
Data Analytics.
Electronics (Basel), 11(3),
421-. https://doi.org/10.3390/electronics11030421
Both Gandomi and Chen are either faculty or part of the University of Technology Sydney. With
Gandomi being a part of engineering and information technology. While Chen is part of data
science. Abualigah is part of the computer science department at Amman Arab University and
the Universiti Sains Malaysia. They all seem qualified in their subject, especially as this source is
peer reviewed. It is not a research source but still helpful. The source covers how machine
learning is in big data applications. It relates to the question as it directly talks about Internet
toxicity and talks about how AI algorithms where it is used to observe users and how they spread
their Internet toxicity. They also talk about meta-heuristic optimization techniques for text
clustering applications which could be helpful as a lot of social media applications are text-
clustering. This technique would also be helpful with AI where it could guide researchers onto
online behaviors in a local setting and then one around the world.
Kamarudin, Y., Mohd Nor, N. A., Libamin, A. C., Suriani, A. N. H., Marhazlinda, J.,
Bramantoro, T., Ramadhani, A., & Neville, P. (2022). Social media use, professional behaviors
online, and perceptions toward e‐professionalism among dental students.
Journal of Dental
Education, 86(8)
, 958–967.
https://doi.org/10.1002/jdd.12912
All of the authors have medical degrees and are qualified dental students at the reputable
universities at the University of Malaya and Airlangga University. This study is peer reviewed
and is a research source. It does not reference any other sources within its study and is published
by a known publisher and is also a fairly new article. This seems like a helpful source as it delves
into the concept of e-professionalism and how in this study in particular, dental students lack that
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professionalism in some aspects. Where negative online behaviors are seen not just in an
entertainment setting which these behaviors could bleed into the consequences of real life. AI
could also be used here as well as these programs could see who is not having positively online
behaviors and disregard users without any nuance.
Mathur, P., Cywinski, J. B., Maheshwari, K., Niezgoda, J., Mathew, J., do Nascimento, C. C.,
Abdelmalak, B. B., & Papay, F. A. (2021). Automated analysis of ambulatory surgery patient
experience comments using artificial intelligence for quality improvement: A patient centered
approach.
Intelligence-Based Medicine, 5
, 100043-. https://doi.org/10.1016/j.ibmed.2021.100043
This source is peer reviewed and the authors are reputable. Where Mathur was the former Health
Minister of Nepal. As well as the text itself being fairly new since it was published only two
years ago. This is a primary source research which is a research finding as it has a study,
conclusions and its analysis. It also does not refer to other sources. Using AI in a medical sense,
participants in the survey have indicated this would be helpful. This source seems to be useful as
they talk about using a NLTK (Natural Language Toolkit) to process natural language. It would
be helpful to dissect the nuances of online behavior as well as knowing the meaning of certain
words that are only commonly used in an online platform.
Olson, Jay A, Sandra, Dasha A, Veissiere, Samuel P. L & Langer, Ellen J. (2023). Sex, age, and
smartphone addiction across 41 countries.
International Journal of Mental Health and Addiction
,
No Pagination Specified. https://doi.org/10.1007/s11469-023-01146-3
Olsen is part of the department of psychology at Harvard and University of Toronto Mississauga.
Sandra is part of the Neuroscience program at McGill University. Veissiere is part of the
department of psychiatry at McGill University. Langer is part of the psychology department at
Harvard. This is peer reviewed but it is not a primary source since it references other studies. It is
still a good source since it talks about a study of problematic smartphone use. It is indicated that
this is higher in younger participants at the age of 26 or younger. It shows how Internet use must
be protected immediately so Internet toxicity does not become a bigger problem for the
individual later in life.
Olenik Shemesh, D., Heiman, T., & Wright, M. F. (2023). Problematic Use of the Internet and
Well-Being among Youth from a Global Perspective: A Mediated-Moderated Model of Socio-
Emotional Factors.
Journal of Genetic Psychology.
, 1–23.
https://doi.org/10.1080/00221325.2023.2277319
Shemesh and Heiman are both associated with the psychology and education department in The
Open University of Israael. While Wright is part of the child study department in Pennsylvania
State University. Where this journal is published seems reputable and as well as the data being
accurate since it was published in 2023. This is peer-reviewed but not a primary source since it
5
references other studies. In the source, they use a sample of middle school children not just in
America but also Israel too. This seems to be a good course since it goes how different online
behaviors are from country to country. The conclusions of the study is that higher PUI
(Problematic Use of the Internet) is higher within adolescence and acknowledges that could
impact their mental health.
Voorveld, H. A. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement with
Social Media and Social Media Advertising: The Differentiating Role of Platform Type.
Journal
of Advertising, 47(1)
, 38–54. https://doi.org/10.1080/00913367.2017.1405754
All three authors are affiliated with the University of Amsterdam in The Netherlands. Voorveld,
van Noort, and Bronner are professors of marketing communications. While Muntinga is a
strategy director in Amsterdam. They seem knowledgeable about the topic and it is a peer-
reviewed article that has been published for a few years. It is a research finding but it is not a
primary research finding. In this, all authors release a study about social media advertisements
and how that would affect consumers. FaceBook and Instagram were seen as the highest for
correlating with negative emotions so because of that they are most likely to engage with Internet
toxicity. They mention if advertisements of social media platforms were changed, the negativity
associated with them could be altered.