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Project 2: Argumentative Research Essay - Coggle Diagram

- - - - "A lack of significance between the irritation caused by the message and advertising value in the advertising acceptance model may be due to three factors: (1) consumer maturity which, based on usage experience, may result in their ignoring more disruptive messages by simply avoiding web pages, applications, or media most likely to engage in these practices, (2) the use of advertisement blockers, the increase of which has been exponential over the past months (40.2% of the sample declared that they use this type of tool), and (3) improved adaptation of advertiser messages, both in terms of content and type of product offered."
      - "Ducoffe introduced a new construct which would be very relevant from that point onwards: advertising value. For this, he proposed a conceptual model that represented the perceived value of advertising for consumers, considering the information provided as well as deceit, entertainment, and irritation caused."
      - "The model at hand is based on the fact that undervalued
        advertising leads to negative consumer responses, whereas highly-valued advertising leads to positive responses that, along with other factors, contribute to the creation of positive attitudes towards advertising. Furthermore, it is considered that the emotions caused by advertising are the basis of those attitudes and the cognitive assessments caused add value to advertising."
    - - The article argues that irritation does not play a major factor in the advertisement value if the advertisement focuses on the other factors(information and entertainment) instead.
      - The article argues that if there is no brand trust present, the risk of irritation is increased, leading to message rejection.
      - The article argues, using the Ducoffe model, that there are three main factors that affect the value of advertising: the information presented, the entertainment value, and any irritation it caused.
    - - This article argues that brand trust does not play a major factor into whether or not the brand's advertising is accepted in the digital space. Instead, the article suggests that brand trust acts more as a barrier against the perceived irritation from the person viewing said advertisement.
    - - All authors are part of a relevant department of a University. Three of the authors are in a marketing research department, and the other is in a communication department. As well, many of the authors have done other scholarly articles before this.
- - - - Algorithmic accountability can be helpful in insuring that something or someone is responsible for the actions of the algorithm itself. Algorithmic transparency can help insure accountability.
      - Algorithmic transparency involves making the aspects of creating and maintaining the algorithm transparent for the public to see and making the information easily accessible so people can know how their information is being used.
      - Algorithms and algorithm-driven content can be beneficial and useful, but produces unintended consequences, such as content filters, echo chambers, and invasions of privacy.
    - - The article explores how algorithms are used to deliver content in an online environment, and the pitfalls of using algorithms. As well, the article explores what algorithmic accountability is, and how algorithmic transparency can help ensure accountability towards the negative aspects of algorithm-driven content.
    - - Both authors are professors at the Myongji University in Seoul, South Korea. As well, this is neither author's first scholarly journal, with both professors having worked on at least 3 before hand.
    - - "The fact that a technical solution, such as reverse engineering, cannot be a sufficient condition for transparency reiterates the challenges that social platforms like YouTube face when claiming its apolitical stance toward content moderation. As discussed earlier, YouTube content moderation via algorithmic filtering intends to diminish hate speech or political extremism. Especially in South Korea, the conspiracy theory that the YMI policy has been abused as a tool for political intervention or suppression is deeply instilled in the minds of some political YouTubers. This example demonstrates that the technological approach is at best a partial solution unless the sociopolitical context is put into consideration" (Par 27).
      - "While algorithm-driven content curations provide online users with convenience and benefits, there are growing concerns about errors, mismatches, and unintended consequences of automations, ranging from creating filter bubbles (Koene et al., 2017; Pariser, 2011), to invading informational privacy (Boyd & Crawford, 2012; Datta et al., 2015), to amplifying human biases and unfairness (Mittelstadt, 2017; Mittelstadt et al., 2016; Schedl et al., 2018). These concerns can be exacerbated by the opaque nature of algorithmic workings and the lack of regulatory frameworks (Pasquale, 2015). In general, individuals do not know what the collection of personal data was based on (i.e., search/purchase history and locations), how or for what purpose their personal information is being exploited by the algorithmic practices, what kinds of biases penetrate machine-learning processes, and the possible discrimination against individual users that could result" (Par 5).
      - "Algorithmic transparency standards regarding the data, model, inference, interface, and human involvement aspects of machine learning algorithms. First, data transparency refers to communicating the purpose of data collection... Second, model transparency is concerned with features that constitute the model trained by statistical algorithms, weighting of each feature, the software used to form the model, and source codes. Third, inference transparency intends quality assurance by disclosing information about the method of prediction and confidence level of the inference. Fourth, interface transparency informs users about the status of algorithmic service, for example, whether it is currently in use or in off mode and whether a user has a right to turn it off, to pause, or to stop the release of personal data. Fifth, transparency of human involvement refers to clarification of which part of the process human judgment is involved in besides the algorithm’s autonomous decision making" (Par 7).
- - - - SNSs use digital nudging (Such as Facebook privacy notifications) to encourage more self-disclosure or to better educate the user over the privacy options available.
      - The article concludes that the current digital nudges are ineffective at encouraging self-disclosure.
      - Self-disclosure and privacy are important parts of social network sites (SNSs). Having a higher privacy, or perceived privacy, can lead to higher self-disclosure amongst users, which is essential for SNSs to function.
    - - Self-disclosure is essential for SNSs to function. Privacy determines how willing a user is to self-disclose information about themselves, either in-person or online. SNSs use digital nudging techniques to try to get users to self-disclose more information, such as completing a profile, adding a new friend, or private messaging another user.
    - - Both authors are members of a Computer Science department at the University of Duisburg-Essen. The article is also very recent, only being less than a year old. As well, both authors have made multiple scholarly articles, with one author having over 13 articles.
    - - Self-disclosure and privacy are essential parts of the human condition and are also integral to Social Network Sites.
    - - "Facebook uses persuasive elements that should apparently trigger the disclosure of further information. Although they might work, they can be seen as manipulative in an adverse sense, whereby self-disclosure becomes less deliberate and potentially regrettable. Furthermore, persuasive strategies that use social
        pressure or even light coercion should be used carefully to avoid causing reactance and promoting mistrust (Kissmer, Potthoff, and Stieglitz 2018). This might particularly be the case if users notice that providers or third parties benefit from the disclosure more than the individual" (Page 14).
      - Privacy concerns have been found to influence self-disclosure (Madden 2012). They are increased when users notice secondary use of their data by e.g. third parties or for marketing purposes (Padyab et al. 2016; Soczka, Brites, and Matos 2015). By contrast, privacy guidelines and information about privacy have been related to lower privacy concerns (Andrade, Kaltcheva, and Weitz 2002; Hui, Teo, and Lee 2007; Krasnova et al. 2010). Knowledge of the actual audience also reduces the expected privacy risk (Masur and Scharkow 2016; Wisniewski, Wilson, and Lipford 2011). In turn, a lower perceived risk has mostly be found to increase self-disclosure" (Page 4).
      - "Privacy campaigns leading to an increased awareness of privacy and settings help users to make informed decisions regarding self-disclosure. Studies have also shown that there is a gap between desired and actual privacy settings (Madejski, Johnson, and Bellovin 2012), thus calling for better means to overcome this gap.
        For similar contexts, when people behave differently
        than they want – e.g. due to a lack of motivation, missing ability or the required effort – Thaler and Sunstein (2009) proposed the idea of nudging, which are changes in the choice architecture that nudge decision-makers towards a certain option" (Page 5).
- - - - Self-disclosure and privacy are both linked together as important aspects of human behavior. Privacy is linked with self-disclosure as an individual's concerns for privacy can influence how much they disclose. If someone is more concerned about their privacy, they might be inclined not to self-disclose information.
      - Studies have shown that people are more willing to self-disclose on the internet, possibly as a side-effect of perceived anonymity online (Which gives the user a higher sense of privacy). This can be abused my marketers looking to use gathered information to increase marketing strategies (targeted marketing, different ads).
      - Self-disclosure is the idea of one person disclosing personal information about themselves to another person. In recent years, with the emergence of the internet and social media, which can require self-disclosure such as birthdates, pictures, videos, etc, the definition of self-disclosure can be widened to include not just another person but another thing (anonymous user, website, bot).
    - - The article explores the application of self-disclosure in
        online situations, utilizing CPM (Communication Privacy Management) theory to bridge interpersonal and online disclosures. The article also establishes a connection between one’s privacy and decisions of how and when to self-disclose, as well as how self-disclosure and privacy can be manipulated online by industry marketers.
    - - Author has a PHD in Public Communication and Technology, and currently teaches Communication Design as a part of the Department of Science and Technology.
    - - If a user trusts a certain brand more, they may be more receptive to any manipulation tactics used by the marketing/advertisements. As well, the information gathered from the user is used to tailor the advertisements towards them, decreasing irritation and increasing entertainment.
    - - "Krasnova et al. (2010) note that, in order to encourage user trust, reliable online social networks attempt to implement fair privacy policies designed to protect users from aggressive marketers. On the other hand, Joinson et al. (2010) note how anonymity (a form of privacy) can increase self-disclosure, since trust issues become increasingly irrelevant if Internet users believe their communications are anonymous.
        As we have seen, there is much evidence to suggest that people exhibit decreased inhibitions about disclosing personal information online (Joinson 2001; Dean 2010). Such findings should be taken as a warning for frequent Internet users; they are probably not surprising to Internet marketers, who are highly aware of the untapped marketing potential from social networking sites and other online venues. The trend toward higher rates of self-disclosure in online versus face-to-face communications simply increases consumer vulnerability to aggressive marketing using information gathered online (Par 23-26)."
      - "Greene, Derlega, and Mathews (2006) have expanded these earlier, dualistic notions of self-disclosure, defining self-disclosure as the “interaction between at least two individuals where one intends to deliberately divulge something personal to another” (p. 411). The process of self-disclosure has also been analyzed by Jang and Stefanone (2011) as the means by which people provide personal information to others, including their thoughts, feelings, and needs. These views stress the interactive nature of self-disclosure by emphasizing how people manage their needs to communicate about themselves" (Par 8).
      - "Privacy and self-disclosure are not just related concepts, but combine to create the assumption of privacy in self-disclosure... Joinson et al. (2010) conclude that an individual’s privacy concerns directly influences that person’s willingness to disclose information online. Further, Krasnova, Kolesnikova, and Guenther (2009) found privacy concerns to be a significant hurdle to online self-disclosure. Social network users, for example, are constantly engaged in protecting some of their private information, while also engaging in and receiving pleasure from online social network activities. Even though a user of an online social network third party application is warned in advance of the potential for collection and utilization of private information, the individual may decide to actively engage in the application, or be unaware of the real possibilities for abuse" (Par 21).
- - - - Algorithms determine the quality and relevance of created content and will sort the content on a user's feed depending on a number of factors (user's likes, shares, comments, etc).
      - Sometimes certain algorithmic factors are weighted more heavily than others, i.e. reactions on Facebook will generate more traction on the post than likes.
      - There are over 4.5 Billion Internet users, and over half use social media. This means that over 2 billiion internet users are creating and viewing content on social media websites.
    - - Algorithms are important because they choose content for users to watch based on user data. Since there is so much content created, algorithms serve as a tool for giving users relevant content to digest. As well, algorithms can be changed to weigh certain factors more heavily, which can be useful to push new tools like Facebook reactions or Instagram reels.
    - - Article is published on the Digital Marketing Institute, which along with the AMA (American Marketing Association), offers courses for marketing certifications. The author worked at the DMI for over 3 years as a content marketing specialist and has had other jobs relating to digital content.
    - - Algorithms give users content based on user data (likes, comments, shares, reposts). This means that an algorithm will likely show content it believes the user will ilke
    - - "As a fallible system, social networks regularly tweak their algorithms to improve user experience. The solution? Ensure your content is high quality, relevant and appealing while keeping an eye on any algorithm changes to see if you need to tweak your social media strategy" (Par 13).
      - "Algorithms are used on social media to sort content in a user’s feed. With so much content available, it’s a way for social networks to prioritize content they think a user will like based on a number of factors.
        As a marketer that may sound wonderful as it gets your content in front of the right people. But be warned, social media algorithms are by no means perfect" (Par 9-10).
      - "As of October 2021, there were approximately 4.5 billion internet users, and of these users, about 57 percent of the world’s population use social media. That’s a massive undertaking to monitor and manage. This is why algorithms are so crucial in determining the validity and placement of social media accounts and content" (Par 7).
- - - - Studies have shown that there are negative associations between algorithmic knowledge and age, as well as level of education. Socioeconomic factors seemed to not have an impact, rather the highest predictor of knowledge being previous experience.
      - The relationship between the user and the algorithm is more than just the user's knowledge that the algorithm exists. How the user interacts with the platform can change based on said knowledge. The user can manipulate the algorithm if they are aware of its inner workings.
      - There are 3 factors that affect how people make sense of algorithms: platform, features, and content type. Depending on the platform and the features that it offers, the algorithm will act different or may weigh certain features more heavily. As well, the type of content that is given to the user affects perception.
    - - There is more to the user and the algorithm beyond the assumptions of how they work. Instead, users and algorithms form a feedback loop of providing and consuming content. This article concludes that user experience is the most predictive factor in concluding algorithmic literacy, and there are 3 factors that dictate how people rationalize different algorithms: platform, certain features (Story reel on Instagram), and type of content.
    - - The author is a member of the Centre for Media and Journalism Studies at the University of Groningen. As well, the author has published 4 other scholarly articles. This article was also published very recently, within 2 years.
    - - "Young people’s sense-making processes of algorithms revolve around three elements. First, platform plays a role in how young people make sense of algorithmic curation. The interviewees in the sample used three to seven different social network sites. Therefore, they could compare how different platforms worked and assess algorithms in relation to each other. A second sense-making strategy is to compare algorithms within an app or website, per feature it offers. For example, participants made a distinction between the personalization of Instagram’s timeline, the Stories section, and its Explore feature. Finally, the type of content (i.e., recommendations vs advertisements vs “regular” content) affects young people’s awareness and perception of the algorithm. Somewhat surprisingly, high algorithmic awareness in one context does not necessarily make users more conscious about potential algorithmic curation in other situations" (Par 26).
      - "As Siles et al. (2020) argue, understanding the reciprocal relationship between users and algorithms goes beyond capturing how audiences assume that algorithms function. It also involves considering the consequences of these algorithmic imaginaries, including the strategies and tactics through which users engage with algorithms and how algorithms become incorporated in everyday life (Lomborg & Kapsch, 2019; Siles et al., 2019; Van der Nagel, 2018). Of course, users’ agency around algorithms is substantially restricted by platform structures (Van Dijck et al., 2018). Yet, they are not completely powerless either. Through both explicit (i.e., the manual personalization tools a platform offers) and implicit actions (e.g., adjusting browsing behavior), users may intervene in their decisions (Haim et al., 2018; Min, 2019; Thurman & Schifferes, 2012). These actions help to shape and refine algorithms of personalized news media, as feedback loops between user behavior and future content exposure emerge (Kitchin, 2017; Thorson, 2020). In relation to news, such expressions of “algorithmic literacy” (Bruns, 2019) are particularly important to consider because they (co-)shape users’ exposure to information and their orientations to public life more generally" (Par 3).
      - "Gran et al. (2020) found significant associations between algorithmic awareness and both users’ level of education and gender. Algorithmic knowledge is also predicted by the level of education and has a negative association with age (Cotter & Reisdorf, 2020). According to the authors, however, socioeconomic factors only account for a small proportion of differences in algorithmic knowledge. The strongest predictor for algorithmic knowledge, they find, is users’ prior experience with algorithms: both their frequency and breadth of use. This matters because much previous work on algorithmic literacy pertains to subgroups that are already relatively digitally literate, such as algorithmic activists (Velkova & Kaun, 2019), web shop owners (Klawitter & Hargittai, 2018), and social media influencers (Bishop, 2019; Cotter, 2019). This raises questions about the algorithmic literacy of “ordinary” users. Cotter and Reisdorf’s (2020) findings support earlier conclusions by DeVito et al. (2018), who characterize building algorithmic literacy as a matter of “learning by doing” (see also Bucher, 2017). Conceptualizing algorithms as “experience technologies” (Blank & Dutton, 2012), these authors argue that algorithms are understood through use" (Par 7).
- - - - Algorithms take in user information (what we like, dislike, repost, or share) and peddle content and information that it thinks will keep the user engaged. This means that algorithms tailor experiences based on the user, which leads to confirmation bias.
      - Fighting confirmation bias is hard, but not impossible. The article suggests liking everything, which would trick the algorithm into delivering different content. As well, the article also suggests seeking out disconfirmation, information that would go against confirmation of a certain belief.
      - Confirmation bias is integral to social media and can be one of its main draws. As the article articulates, social media is dependent on interaction and engagement, and people tend to engage in things that make them comfortable.
    - - Confirmation bias, although integral to social media and one of its tenets, can be harmful to the interacting user and spread mis/disinformation. Confirmation bias is a side effect of algorithms in social media, which prioritize showing the user content they would like to see. In order to combat this, the article suggests either tricking the algorithm (liking everything) or being consciously aware of what information is given and trying to seek 'disconfirmation'.
    - - Wired is a credible source known for its ties to technology and science. The author does not have much innate credibility, but the article deals with common topics (like confirmation bias) and uses many other sources to parse out information.
    - - "There’s no question it’s a lot harder to fight confirmation bias than to just open your apps and let their algorithms control what you see. But in addition to the personal sense of calm a decreased dependence on confirmation bias creates, Lerman’s research also demonstrated that “balanced” feeds cause a positive effect that cascades through social media.
        In essence, posts from a balanced account will be shown more frequently and to a wider array of individuals across all algorithmic spectrums while the platforms attempt to categorize you" (Par 26-27).
      - "Confirmation bias is the natural human tendency to seek, interpret, and remember new information in accordance with preexisting beliefs. Consider it our brains’ default setting... It’s a part of all of us, although once we acknowledge its presence we can take steps to diminish the hold it has on our thinking" (Par 5-6).
      - "Social media companies therefore rely on adaptive algorithms to assess our interests and flood us with information that will keep us scrolling. The algorithms ignore the recency and frequency of what our friends are posting and instead focus on what we “like,” “retweet,” and “share” to keep feeding content that is similar to what we’ve indicated makes us comfortable.
        Social media has removed traditional gatekeepers to information who evaluated stories for newsworthiness and accuracy, and while this has been a boon for the discovery of niche online groups who appreciate the same things you do, it also creates echo chambers in which a user is never presented with alternative perspectives" (Par 10-11).
- - - - Algorithms pose several problems, however. One of the biggest problems is that it can throw the user into a rabbit hole of over-personalized -- or polarized -- content. Additionally, it is much harder to remove radical content than it is to create and post it.
      - There are potential solutions to solve the problem of algorithm polarization. One solution could involve limiting the influence of the algorithm itself. However, the problem with algorithms is tied with social inequalities in society, so solutions must go beyond just algorithmic accountability.
      - Algorithms on SNSs (Social Network Sites) are designed to keep the user engaged on the website. In this way, algorithms store user behaviors and personalize the user experience on the site to keep the user engaged.
    - - This article argues that whilst algorithms are partially responsible for the delivery of radical and extremist content to users because of the nature of how algorithms work, there is a greater societal issue about these ideologies themselves that also needs to be solved alongside algorithmic accountability in order for there to be any meaningful change.
    - - This article was published by PBS, a highly reputable news source. As well, the author holds a PhD and has served at different news institutions, notably the Smithsonian Magazine. She also has published many articles.
    - - Algorithms can serve as echo chambers as it increasingly personalizes, or polarizes, the user's feed to tailor it to what the algorithm interprets as engaging to the user.
    - - "One potential avenue forward might involve placing limits on algorithms, Celis says. Current iterations don’t have a clear cap on the amount of personalization—or polarization—that feeds can offer...
        All this means that addressing our algorithm woes will require more tweaking a few lines of code. Social inequality isn’t a problem that will be solved with an app—and algorithms don’t operate in a vacuum. The best solutions will also require rethinking the larger environment in which these technologies operate. As such, curbing the promotion of extremist ideologies will require partnering computational perspectives with diverse views from the fields of sociology, psychology, public policy, and more" (Par 30-34).
      - "To your garden variety recommendation algorithm, your first dive into a website might present something of a tabula rasa. With nothing to guide its suggestions, a platform will spit out a little of everything: sports, politics, science, kittens. Whatever you do with that content will inform the algorithm’s next move. Over time, as it stockpiles behaviors ranging from the types of articles you share to the amount of time you spend watching a video, the algorithm will start to refine its recommendations.
        The goal, of course, is to keep you coming back—to keep turning profits. That means filtering out anything you might deem a waste of time until the algorithm has effectively personalized your experience of a platform, showing you, in theory, only the things you’d want to see" (Par 10-11).
      - "But with content streaming onto to platforms at unprecedented rates, there’s simply no real capacity to monitor every individual post, never mind how it’s weighted or ranked in the feeds of billions of users. Additionally, these algorithms are far from foolproof: It’s become pretty trivial to engineer fraudulent clicks and views to inflate the amount of screen time an idea receives.
        And the barriers to entry are far lower than those to removal. Policing, flagging, and removing the overwhelming deluge of extremist and radical content is about as fun as it sounds. The people who hold these jobs are “looking at the worst kinds of filth every day,” Broussard says" (Par 26-27).