FIT3152代写、代写Data analytics、R编程设计代做、代写R

日期：2020-05-17 08:51

FIT3152 Data analytics: Assignment 1

This assignment is worth 20% of your final marks in FIT3152.

Activity, language use and social interactions in an on-line community.

Analyse the metadata and linguistic summary from a real on-line forum and submit a report of your

findings. Do the following:

a. Analyse activity and language on the forum over time. Some starting points:

? Describe your data: How active are participants, and are there periods where this increases

or decreases? Is there a trend over time?

? Looking at the linguistic variables, do these change over time? Is there a relationship

between them?

b. Analyse the language used by groups. Some starting points:

? Threads indicate groups of participants communicating on the same topic. Describe the

threads present in your data.

? By analysing the linguistic variables for all or some of the threads, is it possible to see a

difference in the language used by these different groups?

? Does the language used within threads change over time?

c. Challenge: Social networks online. We can think of participants communicating on the same

thread at the same time (for example during the same month) as forming a social network.

When these participants also communicate on other threads, they extend their social

network.

? Can you define, graph and describe the social network that exists at a particular point in

time, for example over one month? How does this change in the following months? Note:

you only need to analyse a short time period overall. We will cover social network analysis

in Lecture 5.

Data

The data is contained in the file webforum.csv and consists of the metadata and linguistic analysis

of posts over the years 2002 to 2011. You will each work with 20,000 posts, randomly selected

from the original file. The linguistic analysis was conducted using Linguistic Inquiry and Word

Count (LIWC), which assesses the prevalence of certain thoughts, feelings and motivations by

calculating the proportion of key words used in communication. See http://liwc.wpengine.com/ for

more information, including the language manual http://liwc.wpengine.com/wpcontent/uploads/2015/11/LIWC2015_LanguageManual.pdf

Create your individual data as follows:

rm(list = ls())

set.seed(XXXXXXXX) # XXXXXXXX = your student ID

webforum <- read.csv("webforum.csv")

webforum <- webforum [sample(nrow(webforum), 20000), ] # 20000 rows

Data fields are (see the language manual for more detail and examples):

Column Brief Descriptor

ThreadID Unique ID for each thread (a group of posts on a theme)

AuthorID Unique ID for each author (-1 is anonymous)

Date Date

Time Time

WC Word count of the text of the post

Analytic LIWC Summary (analytical thinking)

Clout LIWC Summary (power, force, impact)

Authentic LIWC Summary (using an authentic tone of voice)

Tone LIWC Summary (emotional tone)

ppron LIWC (all personal pronouns)

i LIWC ("I, me, mine" words) First person singular

we LIWC ("We, us, our" words) First person plural

you LIWC ("You" words) Second person

shehe LIWC ("She, he, her, him" words) Third person singular

they LIWC ("They" words) Third person plural

number Quantities and ranks

affect LIWC (expressing sentiment)

posemo LIWC (Positive emotions)

negemo LIWC (Negative emotions)

anx Words indicating anxiety

anger Words indicating anger

social Words referring to social processes

family Words referring to family

friend Words referring to friends/friendship

leisure Words referring to leisure

money Words referring to money

relig Words referring to religion

swear Swear words

QMark Question Mark (Punctuation)

Submission. Due 8th May 2020. Suggested length: 6–8 A4 pages + appendix.

Submit the results of your analysis, answering the research questions and report anything else you

discover of relevance. If you choose to analyse only a subset of your data, you should explain why.

You are expected to include at least one multivariate graphic summarising key results. You may

also include simpler graphs and tables. Report any assumptions you’ve made in modelling, and

include your R code as an appendix. Submit your report as a single PDF with the file name

FirstnameSecondnameID.pdf on Moodle.

Software

It is expected that you will use R for your data analysis and graphics and tables. You are free to use

any R packages you need but please document these in your report and include in your R code.

Assessment criteria will include:

The quality of your analysis and description of your analytical process; Graphics and tables

supporting your analysis; The quality of graphics used in the report. Justification of your findings

and the degree of proof you can offer (for example statistical tests); Readability and quality of your

written report; Insights gained from the data; Novelty of your approach.

Factors you should consider (starting points, not a complete list):

Techniques: summary/descriptive statistics, identification of important variables, networks, etc.

Major grouping variables: author, thread, date and/or time., or a combination of these.

Time window (days, weeks, months, years…); Subsets of the data to be analysed.

Graphics to communicate your analysis and insights (histograms, scatterplots, heatmaps, time series

are some basic starting points, but see https://datavizproject.com/ for inspiration.