Social Media Intelligence

The School of Library, Archival and Information Studies (SLAIS)

The University of British Columbia

Course title: Social Media Intelligence

Course Code: LIBR 559D

Program: MLIS, Dual

Year: Winter Session II 2017

Time: Mon. 9:00pm-11:50pm

Location: SLAIS Terrace Lab

Instructor: Dr. Muhammad Abdul-Mageed

Office location: 494

Office phone: 6048-274-530

Office hours: Mon., 1:00pm-3:00pm

E-mail address: muhammad.mageed@ubc.ca

iSchool@UBC Student Portal: http://connect.ubc.ca

1. Course Rationale & Goal:

Rationale/Background:

As social networks like Facebook, Twitter, and YouTube continue to play an important role in mediated communication today, be it at organizational or individual levels, the volume of data generated by their users increase phenomenally. Accordingly, searches and processing of social Web data beyond the limiting level of surface words are becoming increasingly important to business and governmental bodies, as well as to lay Web users. Detection of sentiment, emotion, deception, gender, sarcasm, age, perspective, topic, community, and personality are all valuable social meaning components that promise to be important elements of next generation search engines. The emerging area of extracting social meaning from social network data using automated methods is known as Social Media Intelligence (SMI).

Goal:

This course provides a graduate-level introduction to Social Media Intelligence (SMI) and its associated methods. The goal of the course is to create an interactive learning community around the theme of SMI. This includes familiarizing students with the types and use of social media, and providing hands-on experience in managing, analyzing, and mining social data using available tools (e.g., dashboards) and automated methods (e.g., natural language processing and machine learning technologies). We will read, discuss, and critique claims and findings from contemporary research related to SMI. We will also address practical issues related to successfully managing social media platforms, launching marketing campaigns, and using and building tools to analyze and mine social media.

Potential audiences for this course include, but are not limited to:

People interested in text analytics, information retrieval, information visualization, human-information interaction, natural language processing, machine learning, etc., who want to prepare for, or significantly advance, carrying out research in these fields.
People interested in practical experience on effective usage, management, analytics, and mining of social media in work places (e.g., libraries, museums, companies).

2. Course Objectives:

Upon completion of this course students will be able to:

Understand a wide range of social media usage, management, and mining concepts and tasks and their relevance to the information needs of diverse individuals, communities and organizations. [1.1]
Enhance interpersonal and written communication skills. [2.1]
Collaborate effectively with peers through course assignments. [3.1]
Apply social media marketing, management, and mining methods to address information needs, questions, and issues. [4.1]

3.Course Topics:

Overview of social media types and use and their role;
Running social media systems and campaigns
Social media management
Virality and engagement in social media
Social data crawling and engineering;
Predictive analytics with natural language processing & machine learning;
Practical social media intelligence tasks:
Age and gender detection;
Sentiment analysis and emotion detection;
Personality prediction;
Sarcasm, humor, and occupational class detection;

4. Prerequisites:

MLIS and Dual MAS/MLIS: Completion of MLIS Core or permission of SLAIS Graduate Advisor
Access to a computer: There will be machines in the lab where class is held, but you will need to use your own machine or have access to a machine on a regular basis. You should make your own arrangements for this.

5. Format of the course:

This course will involve lectures, class hands-on activities, individual and group work, and instructor-, peer-, and self-assessment.

6. Course syllabus:

Books:

Buyer, L. (2013). Social PR Secrets: How to Optimize, Socialize, and Publicize Your Brand's News (3rd ed.). ISBN-10: 1938886852, ISBN-13: 978-1938886850.
Nahon, K., & Hemsley, J. (2013). Going viral. Polity. ISBN-10: 0745671284, ISBN-13: 978-0745671284
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O'Reilly Media, Inc. [link]

Suggested Background Books:

Tuten, T. L., & Solomon, M. R. (2014). Social media marketing (2nd ed.). Sage. ISBN-10: 1473913012, ISBN-13: 978-1473913011.
Kawasaki, G., & Fitzpatrick, P. (2014). The Art of Social Media: Power Tips for Power Users. Portfolio. ISBN-10: 1591848075, ISBN-13: 978-1591848073.

Articles & Book Chapters:

Abdul-Mageed, M. (2016). Social Media Mining: Introduction. [Document to be shared with students]
Abdul-Mageed, M., & Diab, M. (2012). AWATIF: A multi-genre corpus for Modern Standard Arabic subjectivity and sentiment analysis. Proceedings of LREC, Istanbul, Turkey. [pdf]
Abdul-Mageed, M. & Diab, M. (2011). Subjectivity and Sentiment Annotation of Modern Standard Arabic Newswire. Proceedings of the the Fourth Linguistic Annotation Workshop. Portland, Oregon, USA, June 23-24, 2011. [pdf] [bib]
Polanyi, L., & Zaenen, A. (2006). Contextual valence shifters. Computing attitude and affect in text: Theory and applications, 1-10. [pdf]
Pang, Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1): 1-135. (Chapters 4 & 5).
Liu, B. 2009. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition,(editors: N. Indurkhya and FJ Damerau).
Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200. [pdf]
Ekman, P. (1999). Basic Emotions. In Dalgleish, T., & Power, M. J. (Eds.). (1999). Handbook of cognition and emotion. Chichester,, UK: Wiley. [pdf]
Saima Aman and Stan Szpakowicz. 2007. Identifying expressions of emotion in text. In Vclav Matouˇsek and Pavel Mautner, editors, Text, Speech and Dialogue, volume 4629 of Lecture Notes in Computer Science, pages 196-205. Springer Berlin / Heidelberg. [pdf]
Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3), 436-465. [pdf]
Rangel, F., Rosso, P., Moshe Koppel, M., Stamatatos, E., & Inches, G. (2013). Overview of the author profiling task at PAN 2013. In CLEF Conference on Multilingual and Multimodal Information Access Evaluation (pp. 352-365). CELCT. [pdf]
Burger, J. D., Henderson, J., Kim, G., & Zarrella, G. (2011, July). Discriminating gender on Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 1301-1309). Association for Computational Linguistics. [pdf]
Ciot, M., Sonderegger, M., & Ruths, D. (2013). Gender Inference of Twitter Users in Non-English Contexts. In EMNLP (pp. 1136-1145). [pdf]
Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media. Journal of Sociolinguistics, 18(2), 135-160. [pdf]
Johannsen, A., Hovy, D., & Sogaard, A. (2015, July). Cross-lingual syntactic variation over age and gender. In Proceedings of CoNLL. [pdf]
Stolcke, Andreas, Ries, Klaus, Coccaro, Noah, Shriberg, Elizabeth, Bates, Rebecca, Jurafsky, Daniel, Taylor, Paul, Martin, Rachel, Meteer, Marie, and Van Ess-Dykema, Carol. 2000. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics 26(3): 339-371. [pdf]
Ritter, A., Cherry, C., & Dolan, B. (2010). Unsupervised modeling of twitter conversations. [pdf]
Zhang, R., Gao, D., & Li, W. (2012, April). Towards scalable speech act recognition in Twitter: tackling insufficient training data. In Proceedings of the Workshop on Semantic Analysis in Social Media (pp. 18-27). Association for Computational Linguistics. [pdf]
Jeong, M., Lin, C. Y., & Lee, G. G. (2009, August). Semi-supervised speech act recognition in emails and forums. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3 (pp. 1250-1259). Association for Computational Linguistics. [pdf]
Plank, B., & Hovy, D. (2015, September). Personality Traits on Twitter--or--How to Get 1,500 Personality Tests in a Week. In 6th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA 2015) (p. 92). [pdf]
Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Dziurzynski, L., Ungar, L. H., Stillwell, D. J., ... & Seligman, M. E. (2014). The online social self an open vocabulary approach to personality. Assessment, 21(2), 158-169. [pdf]
Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 201418680. [pdf]
Farnadi, G., Sitaraman, G., Rohani, M., Kosinski, M., Stillwell, D., Moens, M. F., ... & De Cock, M. (2014). How are you doing? Emotions and personality in Facebook. In Proceedings of the EMPIRE Workshop of the 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP 2014). [pdf]
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., ... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), e73791. [pdf]
Davidov, D., Tsur, O., Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. Proc. of the Fourteenth Conference on Computational Natural Language Learning, pp. 107-116. [pdf]
Gonzalez-Ibanez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in Twitter: a closer look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2 (pp. 581-586). [pdf]
Bamman, D., & Smith, N. A. (2015, April). Contextualized Sarcasm Detection on Twitter. In Ninth International AAAI Conference on Web and Social Media. [pdf]
Althoff, T., Danescu-Niculescu-Mizil, C., & Jurafsky, D. (2014). How to Ask for a Favor: A Case Study on the Success of Altruistic Requests. [pdf]
Ranganath, R., Jurafsky, D., & McFarland, D. A. (2013). Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Computer Speech & Language, 27(1), 89-115. [pdf]
McFarland, D. A., Jurafsky, D., & Rawlings, C. (2013). Making the Connection: Social Bonding in Courtship Situations. American journal of sociology, 118(6), 1596-1649. [pdf]
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Lucas, R. E., Agrawal, M., ... & Ungar, L. H. (2013, June). Characterizing Geographic Variation in Well-Being Using Tweets. In ICWSM. [pdf]
Hovy, D., Johannsen, A., & Sogaard, A. (2015, May). User review sites as a resource for large-scale sociolinguistic studies. In Proceedings of the 24th International Conference on World Wide Web (pp. 452-461). International World Wide Web Conferences Steering Committee. [pdf]
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2012). Mapping the geographical diffusion of new words. arXiv preprint arXiv:1210.5268. [pdf]
Jorgensen, A. K., Hovy, D., & Sogaard, A. (2015, July). Challenges of studying and processing dialects in social media. In Proceedings of the Workshop on Noisy Usergenerated Text (pp. 9-18). [pdf]
Hovy, D. Demographic Factors Improve Classification Performance. [pdf]
Preoţiuc-Pietro, D., Lampos, V., & Aletras, N. (2015). An analysis of the user occupational class through Twitter content. The Association for Computational Linguistics. [pdf]
Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015). Studying user income through language, behaviour and affect in social media. PloS one, 10(9), e0138717. [link]
Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2016). Analyzing Personality through Social Media Profile Picture Choice. ICWSM 2016. [pdf]
Fried, D., Surdeanu, M., Kobourov, S., Hingle, M., & Bell, D. (2014, October). Analyzing the language of food on social media. In Big Data (Big Data), 2014 IEEE International Conference on (pp. 778-783). [pdf]
Boyd, d. m., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), article 11. [link]
Social Media Usage: 2005-2015. [link]
News Use Across Social Media Platforms 2016. [link]
The 15 Best Free Social Media Dashboards and Tools Read more. [link]
9 Social Media Dashboards To Manage Multiple Social Media Profiles. [link]
The 15 Best Free Social Media Dashboards and Tools. [link]
The Guardian: How riot rumours spread on Twitter. [link]
Behind the rumours: how we built our Twitter riots interactive. [link]
Essay: 13 Ways to Make Something Go Viral. [link]
The Six Things That Make Stories Go Viral Will Amaze, and Maybe Infuriate, You. [link]
Engaging Communities: Content and Conversation. [link]
What kinds of local stories drive engagement? The results of an NPR Facebook experiment. [link]
Why You Should Share to Social Media in the Afternoon + More of the Latest Social Media Research. [link]

7. Calendar / Weekly schedule and readings (tentative)

LB= Lisa Buyer book
KN=Karen Nahon book
BKL=Bird, Klein, & Loper book

Week	Date	Topic
1	Jan 9	Course overview; Intro. to social media intelligence/mining

Abdul-Mageed, M. (2016). Social Media Mining: Introduction. [Document to be shared with students]

Week	Date	Topic
2	Jan 16	Social network sites & Social publishing
Social Network Sites:

Boyd, d. m., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), article 11. [link]
Social Media Usage: 2005-2015. [link]
News Use Across Social Media Platforms 2016. [link] Social Publishing:
LB Ch07: The Art & Science of Social Publishing,
LB Ch08: Managing a Community,
LB Ch9: Jump into Any News Story

Week	Date	Topic
3	Jan 23	Social media marketing & management

LB Ch11: Distribution, Application, and Promotion
LB Ch12: The Rise of Visual Reporting
LB Ch13: Scoring Influence
The 15 Best Free Social Media Dashboards and Tools Read more. [link]

Further Readings:

9 Social Media Dashboards To Manage Multiple Social Media Profiles. [link]
The 15 Best Free Social Media Dashboards and Tools. [link]

Week	Date	Topic
4	Jan 30	Introduction to social media analytics with NLP

BKL Ch01-Ch04. [Link]

Week	Date	Topic	Due?
5	Feb 6	Social data crawling and annotation	ASSIGNMENT 3

Abdul-Mageed, M., & Diab, M. (2012). AWATIF: A multi-genre corpus for Modern Standard Arabic subjectivity and sentiment analysis. Proceedings of LREC, Istanbul, Turkey. [pdf]
Abdul-Mageed, M. & Diab, M. (2011). Subjectivity and Sentiment Annotation of Modern Standard Arabic Newswire. Proceedings of the the Fourth Linguistic Annotation Workshop. Portland, Oregon, USA, June 23-24, 2011. [pdf] [bib]
Polanyi, L., & Zaenen, A. (2006). Contextual valence shifters. Computing attitude and affect in text: Theory and applications, 1-10. [pdf]

Week	Date	Topic
6	Feb 13	Family Day (No class)

Watch: [Social Meida Mining with NLP]

Week	Date	Topic
7	Feb 20	Mid-Term Break (No Class)

Week	Date	Topic	Due?
8	Feb 27	Predictive analytics I & Misinformation I	ASSIGNMENT 4

BKL Ch06. [Link]
The Guardian: How riot rumours spread on Twitter. [link]

Further Readings:

Behind the rumours: how we built our Twitter riots interactive. [link]

Week	Date	Topic	Due
9	Mar 6	Predictive analytics II & Misinformation II	Final Project Proposal due

BKL Ch06. [Link]
Zubiaga, A., Liakata, M., Procter, R., Hoi, G. W. S., & Tolmie, P. (2016). Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS one, 11(3), e0150989. [[link] (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0150989&utm_content=buffer5a91c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer#pone.0150989.ref008)]

Week	Date	Topic
10	Mar 13	Applications I: Sentiment

Pang, Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1): 1-135. (Chapters 4 & 5). [pdf in Google Drive]
Liu, B. 2009. Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition,(editors: N. Indurkhya and FJ Damerau). [pdf in Google Drive]

Week	Date	Topic
11	Mar 20	Applications II: Emotion

Saima Aman and Stan Szpakowicz. 2007. Identifying expressions of emotion in text. In Vclav Matouˇsek and Pavel Mautner, editors, Text, Speech and Dialogue, volume 4629 of Lecture Notes in Computer Science, pages 196-205. Springer Berlin / Heidelberg. [pdf]

Week	Date	Topic
12	Mar 27	Applications III: Age & Gender

Rangel, F., Rosso, P., Moshe Koppel, M., Stamatatos, E., & Inches, G. (2013). Overview of the author profiling task at PAN 2013. In CLEF Conference on Multilingual and Multimodal Information Access Evaluation (pp. 352-365). CELCT. [pdf]
Burger, J. D., Henderson, J., Kim, G., & Zarrella, G. (2011, July). Discriminating gender on Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 1301-1309). Association for Computational Linguistics. [pdf]
Ciot, M., Sonderegger, M., & Ruths, D. (2013). Gender Inference of Twitter Users in Non-English Contexts. In EMNLP (pp. 1136-1145). [pdf]
Bamman, D., Eisenstein, J., & Schnoebelen, T. (2014). Gender identity and lexical variation in social media. Journal of Sociolinguistics, 18(2), 135-160. [Google Drive]
Johannsen, A., Hovy, D., & Sogaard, A. (2015, July). Cross-lingual syntactic variation over age and gender. In Proceedings of CoNLL. [pdf]
Nguyen, D., Doğruöz, A. S., Rosé, C. P., & de Jong, F. (2016). Computational sociolinguistics: A survey. Computational Linguistics. [Google Drive]

Week	Date	Topic
13	Apr 3	Age, Gender, & Dialect Presentations; Guest Talk on Social Media Management / Muhammad at EACL

Further Readings:

Personality:

Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 201418680. [pdf]
Farnadi, G., Sitaraman, G., Rohani, M., Kosinski, M., Stillwell, D., Moens, M. F., ... & De Cock, M. (2014). How are you doing? Emotions and personality in Facebook. In Proceedings of the EMPIRE Workshop of the 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP 2014). [pdf]
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., ... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), e73791. [pdf]

Sarcasm, Humor, & Occupational Class:

Davidov, D., Tsur, O., Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. Proc. of the Fourteenth Conference on Computational Natural Language Learning, pp. 107-116. [pdf]
Gonzalez-Ibanez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in Twitter: a closer look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2 (pp. 581-586). [pdf]
Bamman, D., & Smith, N. A. (2015, April). Contextualized Sarcasm Detection on Twitter. In Ninth International AAAI Conference on Web and Social Media. [pdf]
Preoţiuc-Pietro, D., Lampos, V., & Aletras, N. (2015). An analysis of the user occupational class through Twitter content. The Association for Computational Linguistics. [pdf]
Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015). Studying user income through language, behaviour and affect in social media. PloS one, 10(9), e0138717. [link]
Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2016). Analyzing Personality through Social Media Profile Picture Choice. ICWSM 2016. [pdf]

Virality:

KN Ch3, Ch04, Ch05
Essay: 13 Ways to Make Something Go Viral. [link]
The Six Things That Make Stories Go Viral Will Amaze, and Maybe Infuriate, You. [link]
Engaging Communities: Content and Conversation. [link]
What kinds of local stories drive engagement? The results of an NPR Facebook experiment. [link]
Why You Should Share to Social Media in the Afternoon + More of the Latest Social Media Research. [link]

8. Course Assignments:

Assignment	Due date	Weight
ASSIGNMENT 1: Class presentation	10%
ASSIGNMENT 2: Personal readings journal	Throughout first 10 weeks	10%
ASSIGNMENT 3: Analysis and evaluation of a professional social media source	Feb. 6	15%
ASSIGNMENT 4: A practical intelligence task	Feb. 27	20%
ASSIGNMENT 5: Term Project (GROUP)
Proposal	March 6	5%
Report/Product	April 14	40%

Notes about assignments:

All written assignments must be submitted before 11:55 pm of a respective assignment date;
Students are expected to prepare slides for assignment 1 and share with instructor by 12:00/noon the day after their presentation.
Assignment 3 must be submitted in the form of an iPython notebook. Support will be provided on how to create notebooks;
Project proposal/outline must be in a pdf format;
All submitted files should be labeled with your last name(s) followed by an underscore and an assignment code. Assignment codes are two digit numbers, e.g., “assignment01,” “asignment02,” etc.
For example: “abdul-mageed_assignment01.ipynb”

Assignments in Detail

Assignment 1: Class presentation Each student is expected to present once during the course of the semester. For this assignment, each student will choose one or more papers and/or book chapters in consultation with the instructor. The student will then be responsible for preparing slides (e.g., Microsoft PowerPoint or pdf format) for the content and presenting to class in one of the class sessions. The following criteria are required for the presentation:

Students are expected to submit their slides to the instructor by 5:00 pm the day before the presentation is due. The instructor will then make the slides available to the whole class.
Students are encouraged to rehearse two-to-three times before presenting in class, make use of body language, use illustrative examples and visualizations, and deliver their presentation in the assigned time.

Grading: Grading for this assignment will be based on content, presentation skills, clarity of presentation, and ability to answer questions.

Deliverables: Presentation slides and actual presentation Students are expected to submit their slides to instructor and deliver the presentation in class to satisfy this requirement.

Assignment 2: Personal readings journal Students are required to keep a personal readings journal where you summarize and reflect on the weekly readings. The journal should be a single Word document that you update with the new content every week. Each week’s log can be in the range of 300-400 words summarizing the main ideas of the readings. Many of the papers we read apply different marketing, management, and/or mining methods and for each of these you are expected summarize the main points the author makes. For example, for an intelligence task, you are expected to identify the social concept (e.g., emotion), the way it is operationalized, the basic idea of a method used, the datasets exploited, and the results acquired. In addition, you are expected to engage critically by possibly identifying limitations and extensions you can think of. The personal readings journal is meant to keep you involved throughout the course of the term. You are also expected to participate in discussions in class, based on your readings and the journal should help with this regard. You are expected to have a total of 10 logs in the journal, corresponding to the readings of the first 10 weeks. Each week’s log is only accepted if you attend class. If you miss a class, this automatically means you are not allowed to update/submit a log for the session/sessions you missed. As such, failure to attend class without a necessary excuse (e.g., a sickness or another emergency) will result in a lower class grade.

Deliverables: A personal readings journal A total of 10 logs in a single Word document.

Assignment 3: Analysis and evaluation of a professional social media source For this assignment, students are expected to use a rubric that will be provided by the instructor as a basis for conducting a content analysis of a social media source of their choice. The source can be one or more of the social media accounts of a library, museum, company, school, or any other organization. The rubric will guide you for analyzing the social media outlet of the organization (e.g., the Twitter account of the Vancouver Public Library https://twitter.com/VPL) in terms of, for example, frequency and time of postings, media used, interaction with users, topics of posts, network (e.g., which users are followed). Based on your analysis, you are expected to provide a critical evaluation of the extent to which an organization is successful in employing social media to reach its goal, including compared to similar organizations. As a result of your evaluation, you should include a section of recommendations to improve the performance of the organization on the social media platform you analyzed.

Deliverables: A critical analysis and evaluation of a professional social media outlet (~ 2000 words) For this assignment, students are required to provide a written report with their analysis and evaluation. The evaluation should be supported by quantitative evidence as well as a qualitative appraisal of the performance of the source. You should include a brief introduction, follow by the analysis and evaluation (you have the freedom to thematically organize the analysis and evaluation into different subsection), a section on recommendations as described above, and a conclusion. You are expected to follow norms of academic writing and document and support your claims by academic and professional references. You are also encouraged to make use of Tables, figures, screenshots, and visualizations.

Assignment 4: A practical intelligence task This is an exercise involving application of the methods you are trained on in class. For this assignment, you will be given a data set and a practical task to execute. For example, you may be provided a dataset labeled with sentiment tags to build a simple, rule-based system to detect the sentiment. You will be trained in class on similar tasks. Python example code related to similar tasks will be shared by the instructor and discussed with class. Once you are done with this assignment, you are expected to submit the code you wrote as well as a short written description (in a pdf document) of your approach to solving the problem.

The following criteria is required for this assignment:

You must run your code before submitting, and ensure it is working. Also, you should provide a sample in your write up from the output itself (e.g., the first 5-10 lines from a file or a list of words that you extract). For code that writes to a file, at the discretion of the instructor, you may be asked to deliver the output files as well.
For each problem, in addition to your code, you should provide explanatory Python comments as appropriate. Your comments should be short and to-the-point. As a programmer, you should develop a sense as to when to use comments and when not. This requirement is meant to teach you about using comments. Using comments will be discussed in class and feedback on your deliverables will be provided.

Deliverables: Written description + iPython notebooks with code

For each of assignments 3 and assignment 4, you are required to submit a written description (~ 2000 words) and an iPython notebook including your code and relevant narratives/comments, etc.

Assignment 5: Term Project The purposes of this assignment include:

Identifying, analyzing, assessing, and solving a problem via use of social media data;
Applying social media intelligence methods to a practical task of your choice, after consultation with the instructor;
Developing oral and written communication skills through discussions with classmates and instructor;
Demonstrating ability to work as part of a team, including initiative taking, integrity, dependability and co-operation, and effective collaboration.

For this assignment, each student is required to work as part of a group of of 2/3* on a project involving a practical task and usually a significant amount of coding. Example projects include harvesting Twitter data toward a specific topic and building a system of analysis that identifies people’s views (pros and cons) around the topic (e.g., Syrian refugees, gun control, satisfaction with government performance on a given sector). This can be done via use of sentiment analysis, for example. Students can also choose problems for which a gender comparison is expected to yield interesting insights (e.g., gender differences as to affect, activities, topical interests, language styles, personality).

A group of a different size may be possible after consultation with the instructor.

Deliverables Proposal (500-700 words)

Who are the the group members?
What are you mining?
What motivates your work? Why is it important or useful to undertake the chosen task?
How does your project compare to other projects people have conducted in the past?
What are the different steps you will take to ensure success of the project? What are the smaller segments of which the bigger intelligence task is composed? And how will you conduct each small task?
How does the work bread down and what each member of the team be contributing?
Timeline for completing the project, including goals for each segment.

Final Report/Paper (4000-6000 words) The final deliverable should include:

A detailed and clear description of your project, including the necessary sections, as appropriate. For example, you will need to include an abstract, introduction, research questions, a literature review, a description of datasets, implementation details and methods employed, results, and a conclusion involving limitations and future directions;
All relevant code;
All data used, whenever possible;
Pointers to a live version of the project, if any;
As appropriate, you should situate your work within the wider context of previous works and approaches, with supporting arguments (~ 15 sources);
Employment of figures, tables, and visualizations as appropriate to enhance argument and facilitate communicating your findings/results;

9. Course Policies

Attendance: The UBC calendar states: “Regular attendance is expected of students in all their classes (including lectures, laboratories, tutorials, seminars, etc.). Students who neglect their academic work and assignments may be excluded from the final examinations. Students who are unavoidably absent because of illness or disability should report to their instructors on return to classes.”

Evaluation: All assignments will be marked using the evaluative criteria given in this syllabus and also those provided on the SLAIS web site.

Written & Spoken English Requirement: Written and spoken work may receive a lower mark if it is, in the opinion of the instructor, deficient in English.

Access & Diversity: Access & Diversity works with the University to create an inclusive living and learning environment in which all students can thrive. The University accommodates students with disabilities who have registered with the Access and Diversity unit: [http://www.students.ubc.ca/access/drc.cfm]. You must register with the Disability Resource Centre to be granted special accommodations for any on-going conditions. ** Religious Accommodation:** The University accommodates students whose religious obligations conflict with attendance, submitting assignments, or completing scheduled tests and examinations. Please let your instructor know in advance, preferably in the first week of class, if you will require any accommodation on these grounds. Students who plan to be absent for varsity athletics, family obligations, or other similar commitments, cannot assume they will be accommodated, and should discuss their commitments with the instructor before the course drop date. UBC policy on Religious Holidays: http://www.universitycounsel.ubc.ca/policies/policy65.pdf.

Academic Integrity

** Plagiarism** The Faculty of Arts considers plagiarism to be the most serious academic offence that a student can commit. Regardless of whether or not it was committed intentionally, plagiarism has serious academic consequences and can result in expulsion from the university. Plagiarism involves the improper use of somebody else's words or ideas in one's work.

It is your responsibility to make sure you fully understand what plagiarism is. Many students who think they understand plagiarism do in fact commit what UBC calls "reckless plagiarism." Below is an excerpt on reckless plagiarism from UBC Faculty of Arts' leaflet, "Plagiarism Avoided: Taking Responsibility for Your Work," (http://www.arts.ubc.ca/arts-students/plagiarism-avoided.html).

"The bulk of plagiarism falls into this category. Reckless plagiarism is often the result of careless research, poor time management, and a lack of confidence in your own ability to think critically. Examples of reckless plagiarism include:

Taking phrases, sentences, paragraphs, or statistical findings from a variety of sources and piecing them together into an essay (piecemeal plagiarism);
Taking the words of another author and failing to note clearly that they are not your own. In other words, you have not put a direct quotation within quotation marks;
Using statistical findings without acknowledging your source;
Taking another author's idea, without your own critical analysis, and failing to acknowledge that this idea is not yours;
Paraphrasing (i.e. rewording or rearranging words so that your work resembles, but does not copy, the original) without acknowledging your source;
Using footnotes or material quoted in other sources as if they were the results of your own research; and
Submitting a piece of work with inaccurate text references, sloppy footnotes, or incomplete source (bibliographic) information."

Bear in mind that this is only one example of the different forms of plagiarism. Before preparing for their written assignments, students are strongly encouraged to familiarize themselves with the following source on plagiarism: the Academic Integrity Resource Centre http://help.library.ubc.ca/researching/academic-integrity. Additional information is available on the Connect site http://connect.ubc.ca.

If after reading these materials you still are unsure about how to properly use sources in your work, please ask me for clarification. Students are held responsible for knowing and following all University regulations regarding academic dishonesty. If a student does not know how to properly cite a source or what constitutes proper use of a source it is the student's personal responsibility to obtain the needed information and to apply it within University guidelines and policies. If evidence of academic dishonesty is found in a course assignment, previously submitted work in this course may be reviewed for possible academic dishonesty and grades modified as appropriate. UBC policy requires that all suspected cases of academic dishonesty must be forwarded to the Dean for possible action.

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