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Analysis of BoostOR: A Twitter Bot Detection Classification Algorithm

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The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have

The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have affected the field of disaster relief on social media as well. These bots cause problems such as preventing rescuers from determining credible calls for help, spreading fake news and other malicious content, and generating large amounts of content which burdens rescuers attempting to provide aid in the aftermath of disasters. To address these problems, this research seeks to detect bots participating in disaster event related discussions and increase the recall, or number of bots removed from the network, of Twitter bot detection methods. The removal of these bots will also prevent human users from accidentally interacting with these bot accounts and being manipulated by them. To accomplish this goal, an existing bot detection classification algorithm known as BoostOR was employed. BoostOR is an ensemble learning algorithm originally modeled to increase bot detection recall in a dataset and it has the possibility to solve the social media bot dilemma where there may be several different types of bots in the data. BoostOR was first introduced as an adjustment to existing ensemble classifiers to increase recall. However, after testing the BoostOR algorithm on unobserved datasets, results showed that BoostOR does not perform as expected. This study attempts to improve the BoostOR algorithm by comparing it with a baseline classification algorithm, AdaBoost, and then discussing the intentional differences between the two. Additionally, this study presents the main factors which contribute to the shortcomings of the BoostOR algorithm and proposes a solution to improve it. These recommendations should ensure that the BoostOR algorithm can be applied to new and unobserved datasets in the future.

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2018-12

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Social Media: A Case Study in Socially Connected Entrepreneurs

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The purpose of this study is to examine how social connectivity in a collaborative business environment translates to online social communication, namely to social media. Not a lot of academic research focuses in-depth on how startups and entrepreneurs within the

The purpose of this study is to examine how social connectivity in a collaborative business environment translates to online social communication, namely to social media. Not a lot of academic research focuses in-depth on how startups and entrepreneurs within the technology industry perceive social media, or how their work environment can influence the ways they see the role of these platforms. Gangplank was chosen as the subject of this case study based on the emphasis they as a coworking space put on connecting to others in order to accomplish mutual goals. Initial research showed that entrepreneurs using social media did so with a collaborative focus in mind. However, it was unclear if, by developing their businesses in a space devoted to fostering social relationships, entrepreneurs would be more likely to engage and interact with other users on social media platforms. Furthermore, it was unclear if their attitudes toward online and offline communication would be affected by spending time in a dedicated social workspace. In order to find how some entrepreneurs that started or worked closely in the beginning stages of a collaborative, connection-driven workspace used social media and see whether or not they used the platform to establish and build relationships and connect with others, three entrepreneurs from such a workspace were personally interviewed. In these interviews, each entrepreneur gave their personal feelings and opinions on the space itself, their view on the role of social media, and whether or not they connected their space to their social media use. The study also examined each entrepreneur's social media profile on one prominent social network to see how each was practically using the platform, and to analyze how each entrepreneur's use of the platform compared to his perceptions of social media as a whole. The study found that entrepreneurs who became established in a collaboration-oriented space definitely interacted frequently on social media. Each entrepreneur interviewed expressed the importance of working closely with others and forming valuable connections through both online and offline means. These entrepreneurs were established to have followed all the best practices of social media use outlined through research, and to have had a large number of personally engaging interactions and conversations on observed social media platforms.

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Date Created
2015-12

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Twitter Patterns in the Politics of Social Mobilization: #BlackLivesMatter Case Study

Description

The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter is a prominent social media platform used as a political

The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter is a prominent social media platform used as a political medium, contributing to political movements such as #OccupyWallStreet, #MeToo, and #BlackLivesMatter. Using the #BlackLivesMatter movement as an illustrative case to establish patterns in Twitter usage, this thesis aims to answer the question “to what extent is Twitter an accurate representation of “real life” in terms of performative activism and user engagement?” The discussion of Twitter is contextualized by research on Twitter’s use in politics, both as a mobilizing force and potential to divide and mislead. Using intervals of time between 2014 – 2020, Twitter data containing #BlackLivesMatter is collected and analyzed. The discussion of findings centers around the role of performative activism in social mobilization on twitter. The analysis shows patterns in the data that indicates performative activism can skew the real picture of civic engagement, which can impact the way in which public opinion affects future public policy and mobilization.

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Date Created
2021-05

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Reducing Social Media’s Negative Influence on Emerging Adults’ Mental Well-Being with a Design-Focused, Neuroscience Approach

Description

"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping

"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

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Date Created
2021-05

Learning Users Visual Preferences: Building a Recommendation System for Instagram

Description

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can be mediated in order to enhance the user experience for Instagram users. This paper explores methods for creating such a recommendation system. The proposed method employs a learning model called ``Factorization Machines" which combines the advantages of linear models and latent factor models. In this work I derived features from Instagram post data, including the image, social data about the post, and information about the user who created the post. I also collect user-post interaction data describing which users ``liked" which posts, and this was used in models leveraging latent factors. The proposed model successfully improves the rate of interesting content seen by the user by anywhere from 2 to 12 times.

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Date Created
2016-12

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Analysis of the Aftereffects of Terror Attacks on Social Media

Description

Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks

Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.

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Date Created
2016-12

Venture Together: The Dire Need for Social Connection

Description

Since the onset of the COVID-19 pandemic, the world has been turned upside down. People everywhere are recommended to self-isolate and social distance to limit the spread of the deadly virus. Older adults specifically are being forced into isolation because

Since the onset of the COVID-19 pandemic, the world has been turned upside down. People everywhere are recommended to self-isolate and social distance to limit the spread of the deadly virus. Older adults specifically are being forced into isolation because they are at the highest risk for severe illness—illness that can result in hospitalization, intensive care, or even death. But this isolation is not new. Even before COVID-19, the older adult population has been suffering through a social isolation epidemic. And now, with social distancing measures in place, even more adults are being socially isolated to remain safe and healthy. But when individuals are isolated for long periods of time and no longer have an active social network to connect with, this social isolation can become harmful. Social isolation is known to increase the risk of cardiovascular disease, obesity, and stroke, and it is associated with anxiety, depression, and cognitive decline. Furthermore, the risk of premature death from any cause increases because of social isolation. With all these negative consequences, it is crucial that we confront the toll that COVID-19 countermeasures have taken on older adults and look for ways to prevent social isolation. Venture Together, a multi-user social media platform designed for older adults, attempts to do just this and more.

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Date Created
2022-05