DATA419 Assessment 4: Group Research Project

Group project for DATA419: Online Communities and Social Networks (21S1)

Research question:

Can we use a community's structure to predict the growth and future of that community?

Work distrobution:

Network Data: - Kayle

Which topic are we using?

• The five parliamentary parties in the 53rd New Zealand Parliament.
• The MPs these parties currently have.

Where are we getting the data?

• Twitter, from posts within the New Zealand geolocation.
• Data will be collected from Twitter using the tool.

What time period will the data be from?

• The data will be a time series.
• It will be collected in chunks of 7 days (1 week) over a period of 1 year (52 weeks).
• Scraping will work back in time from 17-10-2020 (The end of the 2020 election in NZ).

Network properties to use as predictors: - House

• Average degree: simply the average number of edges per node in the community.
• Modularity: Modularity is a measure of the structure of networks or graphs which measures the strength of division of a network into modules (also called groups, clusters or communities).
• Transitivty: Transitivity is the overall probability for the network to have adjacent nodes interconnected, thus revealing the existence of tightly connected communities (or clusters, subgroups, cliques).
• Motif Distribution: Network motifs are recurrent and statistically significant subgraphs or patterns of a larger graph.
• Density: describes the portion of the potential connections in a network that are actual connections.

Community Detection: - Nana

Methods to detect groups according to different parameters:
• group_components: Group by connected compenents
• group_edge_betweenness: Group densely connected nodes
• group_fast_greedy: Group nodes by optimising modularity
• group_infomap: Group nodes by minimizing description length
• group_label_prop: Group nodes by propagating labels
• group_leading_eigen: Group nodes based on the leading eigenvector of the modularity matrix
• group_louvain: Group nodes by multilevel optimisation of modularity
• group_optimal: Group nodes by optimising the moldularity score
• group_spinglass: Group nodes using simulated annealing
• group_walktrap: Group nodes via short random walks
• group_biconnected_component: Group edges by their membership of the maximal binconnected components

In this project, we are using group_louvain to seperate community.

Defining Growth: - Nana

The definition for community growth:
After the detection of community week by week, Look at the communities from week(n) and week(n+1):
For each community from week(n), repeat the comparation with all the communities from week(n+1):
-> 1, if none of them having intersection, we call the community died;
-> 2, if some of them having certain size of intersection with the chosen community from week(n):
---> 2.a, calculate the proportion of the intersection against the chosen community from week(n).
---> 2.b, if the proportion bigger than the threshold (0.3 for example), we call the community from week(n) "Father", and the community from week(n+1) "Son".

This growth method will make a many-to-many relatonship for father and son community.

Predit: - Selina

• How?

Data structures:

Scraped data:

id	conversation_id	date	user_id	username	mentions	hashtags	replies_count	retweets_count	likes_count	urls	week

(please see the for up-to-date data)

Statistical techniques to use as predictors:

Average Degree	Transitivty Ratio / Clustering Coefficient	3-Motif Distrobution	4-Motif Distrobution	Density

(These predictors will be based on each community within the one-week period)

Inputs required for prediction:

expected Y	predictors	community_id	week	next week expected Y	next week predictors	next week community_id

(Expected Y will either be the number of nodes or the number of edges in the given community for that week)