Insert Your Title Here∗

Insert Subtitle Here

FirstName Surname† Department Name Institution/University Name City State Country [email protected]

FirstName Surname Department Name Institution/University Name City State Country [email protected]

FirstName Surname Department Name Institution/University Name City State Country [email protected]

ABSTRACT

In this sample-structured document, neither the cross-linking of float elements and bibliography nor metadata/copyright information is available. The sample document is provided in “Draft” mode and to view it in the final layout format, applying the required template is essential with some standard steps.

These steps, which should require generation of the final output from the styled paper, are mentioned here in this paragraph. First, users have to run “Reference Numbering” from the “Reference Elements” menu; this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the “Cross Linking” menu.

For accuracy check of the structured paper, user can run the option Manuscript Validation. It informs the user of the wrong or missing values in the paper. The user must correct the paper as per validation messages and rerun Manuscript Validation.

Now, to generate the required layout of the paper, the user should select one of the template styles under the Define Template Style option (choose the required layout design, i.e. choose between Journals and Proceedings).

∗Article Title Footnote needs to be captured as Title Note

†Author Footnote to be captured as Author Note

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

WOODSTOCK’18, June, 2018, El Paso, Texas USA

© 2018 Copyright held by the owner/author(s). 978-1-4503-0000-0/18/06...$15.00

https://doi.org/10.1145/1234567890

Some specific values are required to create a standard layout by choosing a template for the journals or proceedings. So once the user chooses one of the template layout styles, the respective Journal or Conference details dialog box (i.e. journal/conference acronym, DOI, ISBN, copyright, year, etc.) will appear as a prompt during the Define Template Style functionality. The user should fill these values, after which the template creates the desired layout of the paper. The user can now create a PDF of his/her manuscript using the “Save as PDF” option.

If the user is adding any new data, they should make sure to style it as per the instructions provided in previous sections. Carry out the steps for Cross-linking, Fundref data, adding Document History (specific to journal submission), and finally, Manuscript validation and placing the respective metadata (Bibstrip/copyright text)[footnoteRef:2] while applying the required template. [2: The existing Bibstrip data, copyright text and permission block in the sample file are dummy values, so the user needs to provide the correct values required for the submission in the metadata dialog box.]

CCS CONCEPTS

• Insert CCS text here • Insert CCS text here   • Insert CCS text here

KEYWORDS

Insert keyword text, Insert keyword text, Insert keyword text, Insert keyword text

ACM Reference format:

FirstName Surname, FirstName Surname and FirstName Surname. 2018. Insert Your Title Here: Insert Subtitle Here. In Proceedings of ACM Woodstock conference (WOODSTOCK’18). ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/1234567890

1 Insert Heading Level 1

The updated template, user manuals, samples, and required fonts, all are available at the URL https://www.acm.org/publications/proceedings-template. It contains said information for all three versions of MS Word (Windows and 2 versions of Mac). There are also separate links to the user guide, which can be referred to by the user. This URL also contains some useful video links, which describe how to add the template, structure the paper, and generate the layout, in different clips. Display Formula with Number

Lama 2

Karishma Lama

Professor: Justine White

English-1302-82701

28th April 2022

Computer Technology

(Introduction, Counterargument, and Refutation)

Bill Gates the co-founder of Microsoft argued that being born poor was not your mistake, but if you choose to die poor, then that will be the mistake. I believe in what Bill Gates says so, I work hard every day to make my life prosperous and beautiful. I came from a middle-class family, and I know what poverty is; I still remember my parent’s struggle to pay my school fees and feed us. It wasn’t until the year 2007 that I saw a computer for the first time in my school. I have a flashback that my teacher told me a computer is a life-changing machine that makes everyone’s lives easy and creates lots of jobs so, poverty will be eliminated from the world, and I was convinced by his argument that the computer has lots of advantages. However, some scholars differ with this perspective claiming that computers have robbed the world of all valuable jobs. The reasons why computers remain relevant in employment include; bridging the communication gap, reducing the burden of accuracy, maximizing the remote working jobs, and reducing absenteeism. Therefore, in this paper, I am going to major in the impact of computers on employment.

However, a computer is a disaster for illiterate people who have no knowledge of computers or technology; yet there are more illiterate people than the educated ones in the world. Many human jobs have been robbed by computers and technology. Eddy Wu an author of “A World Without Work” reveals that in the future AI technology and computers will continue to advance astronomically and become as powerful that they can do as many jobs as a human can do. In short, the future will be controlled by highly intelligent robots and computer technology (Wu 56). So, computers and technology have big disadvantages which are unseen by many people and huge computer industrialists. They even have the potential of displacing intelligent people and doing all kinds of human jobs. Computer and AI technology create poverty and job losses. In the end, some argue that computers can snatch people’s happiness and joy and pushed them into poverty without notifying them.

It may be true for some people that computers and technology can bring rise to unemployment in the future; but for most people, it can bring massive opportunities with different variations. The article “Computer Occupation: New Applicants New Jobs titles” explains that the computer occupation has been generating a wide variety of jobs as it is advancing and growing every day. The work ranges from academic researchers to complex hardware or software systems to data entry workers and computer equipment repairers. The changes in jobs variation also created new job titles like software developer, data analyst, quality assurance, and more. These jobs are integrated into one title software system so, only a few people were able to get this job now that it’s changed. Furthermore, minimum college-level computer training is the requirement to get the IT jobs but now this culture has been changed too. People who are taught programming in a vocational school or have an associate degree in computer science can also get a job, and they are trained in the work according to their needs. In reality. computers and technology are creating a lot of jobs opportunity of different kinds and helping to eradicate poverty.

Wu, Eddy. “A World Without Work.” Perspectives on Science & Christian Faith, vol. 73, no. 1, Mar. 2021, pp. 56–57. Academic Search Complete, https://search-ebscohost-com.dcccd.idm.oclc.org/login.aspx?direct=true&db=a9h&AN=149566392&site=ehost-live.

“Computer Occupations: New Applications, New Job Titles. (Cover Story).” Occupational Outlook Quarterly, vol. 36, no. 4, Winter 1992, p. 13. Academic Search Complete, https://search-ebscohost-com.dcccd.idm.oclc.org/login.aspx?direct=true&db=a9h&AN=9304260299&site=ehost-live.

Challenger, John A. “Making The Most Of Opportunities.” Vital Speeches of the Day, vol. 71, no. 1, Oct. 2004, pp. 19–23. Academic Search Complete, https://search-ebscohost-com.dcccd.idm.oclc.org/login.aspx?direct=true&db=a9h&AN=14962794&site=ehost-live.

Hussain, Ahsen, et al. “Perceptions and Use of Computer-Assisted Surgery (CAS) in the Orbit.” Orbit, vol. 38, no. 3, June 2019, pp. 180–83. Academic Search Complete, https://doi-org.dcccd.idm.oclc.org/10.1080/01676830.2018.1490440.

Grasedieck, Dieter. “Fit for the Future in a Digitized Europe through Education.” Proceedings of the Multidisciplinary Academic Conference, Dec. 2015, pp. 1–12. Academic Search Complete, https://search-ebscohost-com.dcccd.idm.oclc.org/login.aspx?direct=true&db=a9h&AN=111482216&site=ehost-live.

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Social Networking Analysis Final Project:

The Marvel Universe Social Network

Add Abstract: should not be more than 300 words

INTRODUCTION

In the mid 2017s, the Marvel Cinematic universe (MCU) topped a 12-billion-dollar milestone in box office revenues6 and recently released its trailer for Avengers Endgame which garnered 48.7 million views on YouTube7 in a day of release. It is a passion for many comic book fans and a weekend’s

entertainment for the general public. Our final project is motivated by the inner geek to explore the universe of the marvel heroes and quantify our pre-existing notions about the universe.

Our research question was motivated from our interests and we wanted to analyze the role and influence of each hero in the network and how heroes drive strategic decision making for the studio. In upcoming sections, we will be quantifying the power of the network and see the influence of heroes through differentiation.

Marvel Universe has little over 6,400 heroes (nodes) in the comic world and close to 150 characters in the cinematic universe. This in turns creates a dense network of heroes and there are complexities added to understand the influence of each hero. To begin our analysis, we started with the assumption: heroes have a connection between each other if they appear in the same comic book. Our first step was to take a look at the properties and characteristics of the marvel network by calculating some network dimensions. After the initial analysis, we created network visualization to analyze the ties of the famous

Marvel heroes’ network and for that we used Gephi, a visualization and exploration software. Our analysis also includes the collection of Twitter data and topic modeling.

Related Work

Table 1 – Marvel hero network descriptive statistics

Moving forward, we reviewed the individual influencers of the MCU. Our review involved a detailed analysis of specific nodes to garner some powerful insights. In terms of degree centrality (how well a node is connected in terms of direct connection), Captain America, Spider Man, and Iron Man are the top three heroes. This is proven by the fact that they are the oldest and most successful heroes. Betweenness (how well situated a node is in terms of the paths that it lies on), a type of centrality measure, can also be defined as the number of times of a node passes through the shortest distance between any two nodes in the network. In terms of betweenness centrality, Wolverine plays the most important role in conveying collaborations (information) to other hero in the network who are not connected with each other. In the MCU, Wolverine acquires the control on the collaborations between different clusters since he is the main connection between the X-men and Avengers. Closeness centrality (how close a given node is to any other) represents the ability of spreading information to the whole network within a shortest time frame. Mister Fantastic is the character who has the ability to reach all other hero as quick as possible, since he is the leader of Fantastic Four and the center of the comic universe. Page rank algorithm, (first used by Google) was utilized to calculate the relative score of a webpage’s authority and importance level. Unsurprisingly, Spiderman has the highest page rank centrality since he has the direct connections to other influential heroes such as Iron Man and Captain America. We also notice that all the connected heroes to Spiderman have relatively higher page rank score.

GEPHI PROCESS

In order to gain a further understanding regarding tthe comic network, we created network related visualizations with the help of external software - Gephi. With the assumption that edge strength is based on whether two nodes appear in the same comic , we created the following figure.

Figure 1 - Marvel comic universe network

Figure 1 represents the Marvel comic universe network by ties with different strength. White ties represent weak connections between pure comic heroes, blue ties represent medium connections between comic nodes and movie heroes, and red ties represent strong connections between movie heroes.

Twitter Data Collection

The data collection process was done using “Tweepy” library which consumes behind the scene Twitter API allowing us to scrape tweets based on a given keywords and start posting time of the tweets we are looking for. For this we need to apply for a developer account on Twitter, in order to get an api key and access token which are mandatory for legal data scraping. Due to some limitations on the number of requests we are allowed to do in a time frame, we only used five runs with a sleep time of 15 minutes so that we don’t abuse the scraping process and also not getting blocked by the robot behind. At the end we were able to collect 10000 tweets related to the following keyword : #marvel.

Twitter Data preprocessing

The data preprocessing step was about preparing our collected data to be feeded to models either LDA for topic modeling or sentiment analysis. The process involves transforming the raw data into a clean data by following these steps :

- lowercase text

- remove whitespace

- remove numbers

- remove special characters

- remove emails

- remove stop words

- remove additional stopwords

- remove weblinks and mentions

- lemmatization

After removing all the noise surrounding our tweets, we then move to the most important step which is Topic Modeling,

Topic modeling

The topic modeling process is aiming to cluster our dataset of tweets into some homogeneous topics (clusters) that share the semantic behind using Latent Dirichlet Allocation (LDA), the algorithms takes as parameters the text corpus collected from tweets and the number of topics you want to cluster your data on. Until today there is no hard science allowing us to figure out the the number of topics on which our corpus might be clustered on, so the idea here is to make a hypothesis of a set of k clusters from 2 to 17 for example and start measuring the coherency on each topic, at the end we get some sort of dictionary where keys are the K clusters and values are the coherency metrics “Umass”, and then we pick the “K” value with the highest coherency, that’s the best K clusters your corpus is able to be splitted on. In our case our corpus was having a k around 3 and 4 depending on the execution run.

Twitter Data Analysis

1. Increasing dataset effect:

Increasing the number of tweets will definitely push our corpus to be bigger than the actual one, hence the number of topics covered in the corpus will be increased and diversified, from a sentiment analysis perspective, this will also make the sentiments distribution over text more diverse and non balanced since the data collection doesn't have any rule based approach.

2. Sentiment Analysis

Figure 2 – Sentiment Scores

As we can see in figure 2, there is a high positivity circulating over the sentiments of Marvel related tweets, with over 8000 tweets, while on the other side Positive and Neutral are around 300 for each class.

Figure 3 - Topic Sentiment

As we can see there is a high dominance of positive tweets over the topic_1, topic_2 and topic_3 clusters and this is a very good point that could be interpreted with the good quality of clustering tweets by LDA since it was able to cluster all positive tweets within each other without knowing the sentiment before.

3. Top Hashtags

Figure 4 – Topic Hashtags

The plots above shows that there is a quite similarity between the the most common hashtags especially between topic_2 and topic_3 at the top 3 unigrams, meanwhile the topic_1 still a bit far from those two contexts as shown in the “pyldavis” figure before (refer to notebook), hence we can conclude that there is a correlation between the most used hashtags and the likelihood that a given document belong to a topic.

4. stop-word removal effect:

The stop-words removal is an essential step in Data Preprocessing for NLP text analysis, the main goal is to remove noisy words that won't help the machine learning models in their predictions and keep only those pertinent and meaningful words, in addition the count of stop-words sometimes is very big which might absolutely bias the model into a specific class or a topic.

In the case where stop-words were the most used unigrams on Twitter, our corpus will be very fined in terms of unigrams and the extracted topics will be very meaningful since they won't be using the most common words, but in other side we risk to lose the context of the topic, so it might be a good approach but with a certain threshold.

5. Punctuation removal effect:

An important NLP preprocessing step is punctuation marks removal, these marks - used to divide text into sentences, paragraphs and phrases - affects the results of any text processing approach, especially what depends on the occurrence frequencies of words and phrases, since the punctuation marks are used frequently in text. By keeping them results might be biased due to the noisy data that would still be in our corpus without helping too much either in the clustering process for topic modeling or the prediction of sentiment analysis.

6. Word Stemming effect:

Both stemming and lemmatization provide better results removing semantic duplicates. This allows returning the user more words related to the topic so he can have a better understanding of it. However, stemming adds noise to the results as it includes stems that are not real words, and that's why we used only lemmatization in this process of topic modeling so we could have a better interpretation of the extracted topics.

7. future implementation

As a future improvements on this process, we can try augmenting the dataset and try a new approach for topic modeling with other algorithms like LSI Latent Semantic Indexing which is very powerful in keeping the semantic preserved between clustered topics, also for sentiment analysis we can use transformers who are very powerful on these kind of tasks since they are trained on very large corpus.

Conclusion

Our topic modeling result provides valuable information on heroes and their interactions. It is noted that some heroes are very close in the comics but not in the movies. Marvel should focus on such interactions to gain and do something remarkable to gain audience attention. As phase four of the MCU rolls out beginning 2019, there is a scope of being able to provide the audience with some of these interactions. For example: Falcon and Captain America are considered as a strong duo but in MCU they have had not a strong relationship.