AY: 2019/20

CIS7029 Social Media Analytics for Business

Credit Value: 40

Term / Semester:２　

Module Leader: Dr Esyin Chew

Assessment Brief

Assessment Title:

The Social Media Analytics for Business Research

PRES1 and WRIT1

Contents

Learning Outcomes 3

EDGE 3

Assessment Overview 3

Assessment Component / Weighting: e.g. WRIT1 50% 3

Title: 3

Assessment Requirements / Tasks 3

Assessment Criteria 3

Guidance Notes 4

Submission Details 4

Feedback 4

Marking Criteria 5

Additional Information 6

Referencing Requirements (Harvard) 6

Mitigating Circumstances 6

Unfair Practice 6

Learning Outcomes

This assessment is designed to demonstrate a student’s completion of the following Learning Outcomes:

1. Demonstrate an understanding of concepts underlying social media analytics and be able to apply them appropriately in business settings (Presentation & Written Paper);
2. Critically evaluate and implement specialist technologies to harvest, analyse and visualise “social data” from individuals to corporate perspectives (Presentation & Written Paper);
3. Synthesise and apply social analytics and appropriate techniques on social information (Presentation & Written Paper);
4. Critically evaluate, design, prototype and implement social media applications and visualization for business story telling (Written Paper).

EDGE

The Cardiff Met EDGE supports students in graduating with the knowledge, skills, and attributes that allow them to contribute positively and effectively to the communities in which they live and work.

This module assessment provides opportunities for students to demonstrate development of the following EDGE Competencies:

ETHICAL	Critical understanding of the importance of ethical, social, cultural and legal agendas in social media analytics.
DIGITAL	Advanced digital skills for the fourth industrial revolution: social big data harvesting, visualisation and business story telling.
GLOBAL	International horizon and benchmarking for social media analytics corporate specialist software (Tableau) and Python programming knowledge. The assessment involves creating online Tableau visualisations on Tableau’s cloud system to be shared with the world and as a continued portfolio for students.
ENTREPRENEURIAL	Use students’ creativity to solve business and/ or public problems and spot social media entrepreneurship opportunities. The Dragons’ Den assessment is aimed to develop entrepreneurial characteristics.

Assessment Requirements / Tasks (include all guidance notes)

Assessment Components

There are TWO assessment components you must complete for this module with a combined total of 100% weighing. The separate components weigh as follows:

1. Part One: Presentation (PRES1) – 20% weighting

2. Part Two: Written paper on practical project (WRIT1) – 80% weighting

Part One

Presentation (PRES1) – 20% weighting (500 word equivalent)

You will be required to deliver a three minute Dragons’ Den in Week 8-10 in your seminar sessions with your tutor and dragons (selected peers by tutor).

You will play a role of a “social media entrepreneur” with only three minutes to pitch your “Social Media Analytics for Business” ideas to the “dragons” (selected peers by your tutor), pretending multimillionaires who are willing to invest their own cash to kick-start the business. After each pitch, the Dragons have the opportunity to ask questions about your social media analytics proposal. The times will be strictly managed: A 3 minute presentation followed by 2 minutes Q&A.

You will specifically be required to present the:

• Aims and objectives of your proposed social media analytics for business
• The project background (current problem statements, project motivation and etc.)
• Your initial design of how your social media analytics research project for business will be conducted
• Discussion on the initial results and visualisation obtained (if applicable).
• The proposed project scope, initial designs and any work-in-progress (i.e. third-party tools / APIs / code demo can also be presented)
• Importantly, the “so what” insights of the business story telling must be presented – i.e. the value for money of your business pitch!

The subject of the social data scrapping and the results analysed are left for the student to choose. For examples (and not limited to):

• You could scrap for keywords related to some corporate data, e.g. KFC, Tableau or any company of your choice on its corporate review website and look for customers’ experiences or customers’ views analytics.
• You could scrap for keywords related to some public data on Twitter, i.e. McDonald, Starbucks or any company of your choice and look for public perceptions, market trends or brand monitoring.

• You could scrap customers’ reviews for products data from Asda, Tesco, Amazon, or any online shop for competitor analysis or product review comparison.
• You could harvest overall sentiment towards an international brand or a global company and provide data analytics and visualisations for a business story telling.
• Any social media analytics topic of your own choice, e.g. the company you worked for.

Please ensure you discuss your topic with your tutor in the studio/seminar as early as possible before you begin working on it.

Part Two

Written paper on practical project (WRIT1) – 80% weighting due in Week 11

You are required to write a 3,500 word (with 10% over/under) research paper based on your practical project for this module. The paper should contain rigorous evidence and references from the primary and secondary data collection you undertook and the analysis and findings of your practical project proposed from Part 1 – PRES1.

Rationale

As part of practical project in this module and learnt from the weekly studios, you will be harvesting a suitable dataset using the relevant tools, i.e. Tableau, Python, Facebook or Twitter API or third party Tool(s) and extracting relevant information from the results, as proposed in your Part 1 Business Pitch.

Therefore, you should start your paper with the motivation or rationale of your research, especially the business aim of your project. The design and approach of your project such as the reasons for the choice of social web harvesting, scope for strategic or tactical decision making, business values, public interest, marketing campaign, product reviews, branding and marketing, customers’ preferences or other etc.

Research Tools and Methods

You need to discuss your research into suitable tools and/ or APIs and the justification for your choice. Based on this, you should then document the design of your project and show clearly how your research project communicates with any third-party service or API.

Results and Visualisation

You should discuss the results with necessary business or social implications and relate that back to the motivation or rationale of your project. Visualisation is very important, so the report should also contain suitable visualisations for your business storytelling out of the social data you collected.

Limitations and Implications

In addition, project limitations and recommendations are desirable.

Conclusion and Appendices

Finally, draw a conclusion with key results to nicely conclude your web harvesting research.

You are encouraged to compare various technical tools and techniques to demonstrate the social media analytics skills you have learnt.

You can implement your social web harvesting research project using any suitable language / technology / third-party tools you see fit. You can hard-code queries or you can provide a suitable front-end where users can enter search keywords. In addition, you can show the results in any suitable form, e.g. tables, various forms of innovative graphs or information overlaid on a map. The final visualisation needs to be published on Tableau Public and include the link and evidences in the paper as Appendixes.

Additional Information

Referencing Requirements (Harvard)

The Harvard (or author-date) format should be used for all references (including images).

Further information on Referencing can be found at Cardiff Met’s Academic Skills website.

Assessment Criteria

Part 1 (PRES1): Three Minutes Dragons’ Den as a Proposal for the Part 2	20%
Innovation, creative and clear presentation	5%
Understanding of the social media analytics for business design and anticipated implementation, APIs and techniques used	10%
“So What?” aspects for the business value / anticipated research impact	5%
Part 2 (WRIT1): Written Research Paper of the Practical Project	80%
Title Page: A title of not more than eight words should be provided and the student’s name and student ID. Biography: a brief professional biography of not more than 100 words about you. Abstract (less than 200 words): students must supply a structured abstract in their submission, set out under 4-7 sub-headings Purpose (mandatory) Design/methodology/approach (mandatory) Findings (mandatory) Social or Business implications: the original value of the research (mandatory) Research limitations/implications (if applicable)	10%
Introduction: Motivation or Rationale of the project	5%
Design and discussion of data sources / tools/ APIs and justification of choice	10%
Key Results, Visualisation and Business Story Telling	15%
Conclusions, Project Limitation and Recommendation	10%
References and Report Format	10%
Appendixes, e.g. A link to Tableau Public of your visualisation work; Screen shots of data source file, interfaces or third party tools you used or Source code. Please zip everything and submit via a separate submission link on Moodle. Feel free to put the key screen shots in the main report as well.	10%
Total:	100%

Submission Details

Please see Moodle for confirmation of the Assessment submission date.

Submission will be by 4:00pm on the deadline day (Week 11).

Any assessments submitted after the deadline will not be marked and will be recorded as a Non-Attempt.

Submit the assignment online through Moodle, including

1. a research paper (include the Tableau public link, a link to the Youtube Presentation, references and appendixes);
2. a zip folder including final presentation slides and appendixes, such as the source files, e.g. Excel sheets, interfaces or third party tools you used or Python source code.

The assessment must be submitted as a zip file / pdf / word document through the Turnitin submission point in Moodle

Submit the assignment online through Moodle, including

1. a research paper (include the Tableau public link, a link to the Youtube Presentation, references and appendixes);
2. a zip folder including final presentation slides and appendixes, such as the source files, e.g. Excel sheets, interfaces or third party tools you used or Python source code.

The assessment must be submitted as a zip file / pdf / word document through the Turnitin submission point in Moodle

Summative feedback for the Part 2 assessment will be provided electronically via Moodle, and will normally be available 4 working weeks after initial submission. The feedback return date will be confirmed on Moodle.

Feedback will be provided in the form of a Tunitin-GradeMark rubric and supported with comments on your strengths and the areas in which you could improve.

All marks are preliminary and are subject to quality assurance processes and confirmation at the Examination Board.

Further information on the Academic and Feedback Policy in available in the Academic Handbook (Vol 1, Section 4.0)

Marking Criteria for PRES1

Marking Criteria for WRIT1

Mark Range	Criteria
80 – 100	An excellent research paper is given that shows evidence of extensive research: brilliant motivation/rationale of the project with an excellent design, discussion of data sources / APIs and justification of choice. A well written, efficient, functional project for Social Media Analytics for Business Research Project has been developed and thorough findings with visualisations and storytelling are evident. Excellent key results with limitations and conclusions are depicted. An excellent presentation is also given and the student demonstrates a brilliant understanding of the implementation, APIs and techniques used. The format and references are at the top end of this band – the work is of publishable quality.
70 – 79	An excellent academic paper is given that shows evidence of detailed research: brilliant motivation/rationale of the project with an excellent design, discussion of data sources / APIs and justification of choice. A well written, efficient, functional Social Media Analytics for Business Research Project has been developed with clear evidence of findings with visualisations and storytelling. A clear presentation is also given and the student demonstrates an excellent understanding of the implementation, APIs and techniques used. The format and references are excellent with minor typos and errors.
60 – 69	A very good report is given that shows evidence of detailed research: good motivation/rationale of the project with a detailed design, discussion of data sources / APIs and justification of choice. A well written, functional Media Analytics for Business Research Project has been developed with good evidence of findings with visualisations and storytelling. A clear presentation is also given and the student demonstrates a very good understanding of the implementation, APIs and techniques used. The format and references are very good with some typos and errors.
50 – 59	A good research paper is given that shows evidence of research: some motivation/rationale of the project with a good design, discussion of data sources / APIs and justification of choice, though these could be expanded upon. A functional Social Media Analytics for Business Research Project has been developed with some evidence of findings with visualisations and storytelling. However, the visualisations and discussion could be enhanced. A good presentation is given and the student demonstrates a good understanding of the implementation, APIs and techniques used. The format and references are good with some typos and errors.
40 – 49	A basic research paper is given that shows some evidence of research: limited or no motivation/rationale of the project with a basic design, limited discussion of data sources / APIs and justification of choice – these need to be expanded upon. A functional Social Media Analytics for Business Research Project has been developed but there is little to no evidence of visualisations. Only a basic presentation is given and the student only demonstrates a basic understanding of the implementation, APIs and techniques used. The format and references are basic with major typos and errors.
35 – 39	A very basic research paperis given that shows little to no evidence of research: no motivation/rationale of the project with only a very basic design, data sources / APIs and justification of choice. A limited Social Media Analytics for Business Research Project has been developed that does not meet all functional requirements and little to no evidence of testing and visualisations is evident. Only a basic presentation is given and the student demonstrates little to no understanding of the implementation, APIs and techniques used. The format and references are poor with major typos and errors.
Under 35	Limited or no evidence of research: no motivation/rationale of the project with no design, data sources / APIs and justification of choice is given. No functional Social Media Analytics for Business Research Project has been submitted with no testing and visualisations and no meaningful results produced. No presentation is given. The format is very poor and no or little references with major errors.

Mitigating Circumstances

If you have experienced changes or events which have adversely affected your academic performance on the assessment, you may be eligible for Mitigating Circumstances (MCs). You should contact your Module Leader, Personal Tutor or Year Tutor in the first instance.

An application for MCs, along with appropriate supporting evidence, can be submitted via the following link to the MCs Dashboard

Applications for MCs should ideally be submitted as soon as possible after circumstances occur & at the time of the assessment. Applications must be submitted before the relevant Examination Board.

Further information on the Mitigating Circumstances procedure is available in the Academic Handbook (Volume 1, Section 5)

Unfair Practice

Cardiff Metropolitan University takes issues of unfair practice extremely seriously. The University has distinct procedures and penalties for dealing with unfair practice in examination or non-examination conditions. These are explained in full in the University’s Unfair Practice Procedure (Academic Handbook: Vol 1, Section 8)

Types of Unfair Practice, include:

Plagiarism, which can be defined as using without acknowledgement another person’s words or ideas and submitting them for assessment as though it were one’s own work, for instance by copying, translating from one language to another or unacknowledged paraphrasing. Further examples include:

• Use of any quotation(s) from the published or unpublished work of other persons, whether published in textbooks, articles, the Web, or in any other format, which quotations have not been clearly identified as such by being placed in quotation marks and acknowledged.
• Use of another person’s words or ideas that have been slightly changed or paraphrased to make it look different from the original.
• Summarising another person’s ideas, judgments, diagrams, figures, or computer programmes without reference to that person in the text and the source in a bibliography or reference list.
• Use of services of essay banks and/or any other agencies.
• Use of unacknowledged material downloaded from the Internet.
• Re-use of one’s own material except as authorised by the department.

Collusion, which can be defined as when work that that has been undertaken with others is submitted and passed off as solely the work of one person. An example of this would be where several students work together on an assessment and individually submit work which contains sections which are the same. Assessments briefs will clearly identify where joint preparation and joint submission is specifically permitted, in all other cases it is not.

Fabrication of data, making false claims to have carried out experiments, observations, interviews or other forms of data collection and analysis, or acting dishonestly in any other way.