User:Siddkumaran/sandbox

Article Evaluation
Wikipedia Article: "Casey Neistat"

Casey Neistat

Notes:


 * Everything is related to the person of interest
 * It covers the entire history of the person (past and present)
 * Nothing seems to be out-of-date
 * All recent events are included in the person's life
 * It is well summarized and nothing is too distracting
 * All information is clearly presented and in a logical manner
 * Since it is an article about a famous (YouTube) influencer, there is no bias in the facts that are presented
 * The article is completely neutral
 * No viewpoint is overrepresented or underrepresented
 * The (tested) links to sources work
 * Includes primary sources from the person of interest himself
 * Also, sources from primary interviews
 * Many unreliable sources
 * Lots of un-credible news sources
 * Any opinionated statements about the person of interested are clearly sourced
 * On the "Talk" page, a editor mentioned an incorrect fact in the article and asked someone to verify
 * Also, there are some sections that some editors want to add in that are mentioned in the "Talk" page
 * Also, some justifications for some removals
 * It is apart of multiple WikiProjects
 * WikiProject Biography
 * WikiProject Actors and Filmmakers
 * WikiProject YouTube
 * It is rated a C
 * Because a lot of substantial information is missing
 * More reliable sources are needed

Link to My Comment on the Talk Page: Talk:Casey Neistat

Link to My Section on the Talk Page: Talk:Casey Neistat

Possible Articles to Work On
Potential Topics:


 * Fake News
 * Conspiracy Theories
 * 2020 Campaign

Potential Articles:


 * 2020 United States elections
 * Add more information on candidates
 * Provide more background context in 2020 race
 * Update the views of the candidates based on primary evidence
 * Barack Obama citizenship conspiracy theories
 * Add more origins for the start of the conspiracy theory
 * Fake news
 * Add more historical context as to how fake news originated
 * Provide more examples of fake news

Chosen Article: Fake News
In the original article, there is a section about fake news in the 21st century. Much of the current version talks about the origins and presence of fake news on the internet. The sections ends by mentioning the usage of the word "fake news" by Donald Trump and the impact on the 2016 election.

However, since the 2016 election, fake news became a very controversial topic. Since then, a lot of mainstream news outlets and social media companies have been criticized for publishing and sharing fake news to the public.

We want to focus on adding a section about the research going on in detecting fake news in mainstream media. There has been a lot of data science and ML research that have been conducted regarding detecting and identifying fake news in mainstream media and social media. Additionally, researchers found interesting indicators and patterns to pinpoint and combat fake news on the news or social media.

A lot of this interesting research about fake news have been left out in this section of article. We want write a few paragraphs on this section highlighting the some of the research regarding fake news and summarizing the results of the research. We also want to write a bit about how these studies and research is helping combat fake news in mainstream and social media.

Relevant Sources:

 * 1) Fake News Detection on Social Media: A Data Mining Perspective (Source: SIGKDD)
 * 2) Less than you think: Prevalence and predictors of fake news dissemination on Facebook (Source: Science Advances)
 * 3) Detecting fake news at its source (Source: MIT News)
 * 4) How Fake News Goes Viral: A Case Study (Source: The New York Times)
 * 5) Combating Fake News: An Agenda for Research and Action (Source: Harvard Kennedy School)

First Draft of Article
How Fake News Spreads and Goes Viral:

Fake news has the tendency to become viral among the public. With the presence of social media platforms like Twitter, it becomes easier for false information to diffuse quickly. Research has found that false political information tends to spread “3 times” faster than other false news. On Twitter, false tweets have a much higher chance of being retweeted than truthful tweets. More so, it is humans who are responsible in disseminating false news and information as opposed to bots and click-farms. The tendency for humans to spread false information has to do with human behavior; according to research, humans are attracted to events and information that are surprising and new, and, as a result, causes high-arousal in the brain. This ultimately leads humans to retweet or share false information, which are usually characterized with clickbait and eye-catching titles. This prevents people from stopping to verify the information. As a result, massive online communities form around a piece of false news without any prior fact checking or verification of the veracity of the information.

Research on Fake News:

Since the 2016 presidential election, fake news has been a popular topic of discussion by President Trump and news outlets. The reality of fake news had become omnipresent, and a lot of research has gone into understanding, identifying, and combating fake news. Also, a number of researchers began with the usage of fake news to influence the 2016 presidential campaign. One research found evidence of pro-Trump fake news being selectively targeted on conservatives and pro-Trump supporters in 2016. The researchers found that social media sites, Facebook in particular, to be powerful platforms to spread certain fake news to targeted groups to appeal to their sentiments during the 2016 presidential race. Additionally, researchers from Stanford, NYU, and NBER found evidence to show how engagement with fake news on Facebook and Twitter was high throughout 2016. Recently, a lot of work has gone into detecting and identifying fake news through machine learning and artificial intelligence. In 2018, researchers at MIT’s CSAIL (Computer Science and Artificial Intelligence Lab) created and tested a machine learning algorithm to identify false information by looking for common patterns, words, and symbols that typically appear in fake news. More so, they released an open-source dataset with a large catalog of historical news sources with their veracity scores to encourage other researchers to explore and develop new methods and technologies for detecting fake news. Despite the ongoing research at top universities and institutions, there is much debate on the effectiveness of such technology in identifying fake news. There is still not enough good training data for machine learning and AI scientists to use to create very accurate predictive models on detecting fake news. Nonetheless, a lot of research is still ongoing to better understand fake news and their characteristics.

Detecting Fake News:

Fake news has become increasingly prevalent over the last few years, with over a 100 incorrect articles and rumors spread incessantly just with regard to the election. These fake news articles tend to come from satirical news websites or individual websites with an incentive to propagate false information, either as clickbait or to serve a purpose. Since they typically hope to intentionally promote incorrect information, these articles are quite difficult to detect. When identifying a source of information, one must look at many attributes, including but not limited to the content of the email and social media engagements. The language, specifically, is typically more inflammatory in fake news than real articles, in part because the purpose is to confuse and generate clicks. Furthermore, modeling techniques such as n-gram encodings and bag of words have served as other linguistic techniques to determine the legitimacy of a news course. On top of that, researchers have determined that visual-based cues also play a factor in categorizing an article, specifically some features can be designed to assess if a picture was legitimate, and provides us more clarity on the news. There is also many social context features that can play a role, as well as the model of spreading the news. Websites such as “Snopes” try to detect this information manually, while certain universities are trying to build mathematical models to do this themselves.

Combating Fake News:

Fake news can be very dangerous with promotion of false information and the ability to influence public opinions. With the prevalence of social media and other digital media platforms, it requires a lot of effort to cut off the spread of fake news. First of all, consumers should critically examine the legitimacy of sources when reading in online platforms. In addition to consumer’s self-awareness, technology companies should invest money and effort into developing programs to identify and detect fake news. It also depends on journalists and news agencies to create reliable contents so consumers can access to accurate information. If more media agencies and publishers can be held accountable for their content, combined with technological advancement, these strategies enable everyone to identify fake news and make judgments based on legitimate information.

Popularity of Fake News:

Fake news has gained lots of popularity with various media outlets and platforms. Researchers at Pew Research Center discovered that over 60% of Americans access news through social media compared to traditional newspaper and magazines. With the popularity of social media, individuals can easily access fake news or similar content. One study looks at the number of fake news articles being accessed by viewers in 2016 and found that each individual was exposed to at least one or more fake news articles daily. As a result, fake news is extremely omnipresent among the viewer population and results in its ability to spread across the internet.

How Fake News Spreads and Goes Viral:
Maybe the group could break this paragraph into two after "More so, it is humans..." And starting the paragraph with "Humans have more power to disseminate..." instead. The reason being I think these this part and the content prior are slightly different although related, with the latter more focused on human behavior.

Research on Fake News:
Similar to my suggestion for the previous section, I would break this into parts to make it clearer. I would start a new paragraph after "Also, a number of researchers began..." and omit "also" from the sentence. And then again at "Despite the ongoing research at...". Moreover, the team used the AI acronym without placing (AI) after the first mention of artificial intelligence.

Detecting Fake News:
'Specifically' was used to begin a sentence but the first letter was not capitalized.

Combating Fake News:
Maybe the team could give more context or an example as to why fake news could be dangerous? Also, I think this section could come before the previous one.

Responses to Peer Reviews
In order to best respond to the feedback, our group will be addressing the comments section by section, and then talk about any overall feedback or higher level comments.

Detecting Fake News: For this section, we got several significant comments that we will use to improve our article. As mentioned, some of the techniques used are not necessarily clearly defined. However, we believe defining them at an individual level would take too much away from the focus of the article. Instead, we will include direct links to these techniques Wikipedia pages. We will also make the grammatical changes suggested by Isabel and Ariel, as well as provide the details of the 2016 election. Finally, we will explore adding an example here.

Research on Fake News: In this section, one comment we received was that we should explain more of the techniques for this. However, we believe that we do address this at later points, especially with the combating fake news section. We could potentially combine theses sections, but otherwise think the separation is good as is. We really liked Hannah’s changes, and will definitely rephrase or delete the last two sentences to prevent implication of biases.

Popularity of Fake News: For this section, we did not receive many comments, but we will fix the grammar mistakes noted, and reconsider the word virally.

How Fake News Spreads and Goes Viral: The biggest theme we saw from these comments is that we should talk about more social media sources, and their impact. We also realized through the feedback that part of this section may seem to be argumentative, and work to make sure it is a more neutral stance

Combating Fake News: This section seemed to be our most problematic one, as multiple students rightly pointed out that it was more opinionated than factual. We think our best option would be to maybe remove this, and include this information as a call to action for our Move Me assignment. We do believe it's important, but think on Wikipedia, only actual confirmed efforts should be included, instead of high-arching suggestions.

All in all, we really appreciate all the feedback and have two things to focus on in improving our article: grammar/diction and preventing bias from entering our article. Furthermore, we must consider how we will incorporate this into the actual page.

2nd Draft of Article
Fact-checking Education (addition to Fact-checking Wikipedia article)

With the circulation of fake news on the internet, many organizations have dedicated time to create guidelines to help read to verify the information they are consuming. Many universities across America provide university students resources and tools to help them verify their sources. Universities provide access to research guides that help students conduct thorough research with reputable sources within academia. Organizations like FactCheck.org, OntheMedia.org, and PolitiFact.com provide procedural guidelines that help individuals navigate the process to fact-check a source.