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Related Experiment Video

Updated: Feb 26, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Link prediction on Twitter.

Sanda Martinčić-Ipšić¹, Edvin Močibob¹, Matjaž Perc^2,3

¹Department of Informatics, University of Rijeka, Rijeka, Croatia.

|July 19, 2017

Summary

This summary is machine-generated.

Twitter data analysis reveals that hashtags effectively capture tweet context for link prediction. This social network analysis demonstrates robust performance even with incomplete data, highlighting the utility of hashtags in understanding online behavior.

Related Experiment Videos

Last Updated: Feb 26, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Area of Science:

Social network analysis
Computational social science
Data science

Background:

Twitter is a major social networking service with over 300 million users.
Its open data access supports research in social interactions, sentiment analysis, and collective behavior.
Understanding network dynamics and predicting future connections are key research areas.

Purpose of the Study:

To construct and analyze co-occurrence language networks from Twitter data.
To evaluate various link prediction methods, including novel weighted similarity measures.
To compare the effectiveness of hashtag-based versus all-word networks for semantic context analysis.

Main Methods:

Construction of co-occurrence language networks using hashtags and all words from tweets.
Application of five established link prediction methods and two proposed weighted similarity measures.
Evaluation of algorithms using multiple datasets and performance metrics, including ranking diagrams.

Main Results:

Hashtag networks provide results comparable to all-word networks, indicating robust semantic context capture.
Ranking diagrams effectively compare the performance of different link prediction algorithms.
Successful link prediction algorithms accurately forecast probable links even with incomplete network information.

Conclusions:

Hashtags are a suitable and efficient tool for studying Twitter content and categorization.
Link prediction models demonstrate resilience and effectiveness even when network data is limited.
The findings support the use of simplified network representations for analyzing large-scale social media data.