This document summarizes research on detecting Twitter bots that share SoundCloud music tracks. The researchers analyzed over 11.5 million tweets from 2 million accounts sharing SoundCloud URLs. They identified metrics to measure account diversity, tweet originality, tweet text diversity, and SoundCloud URL diversity for tracks and accounts. Preliminary analysis showed correlations between low metrics may indicate bot-like retweeting behavior, while higher metrics suggested more original sharing. Further analysis of larger datasets could help validate the metrics to develop bot detection approaches on SoundCloud.

1. @qutdmrc
Detecting Twitter Bots That Share SoundCloud Tracks
Axel Bruns, Brenda Moon, Felix Victor Münch, Patrik Wikström
Digital Media Research Centre, Queensland University of Technology
Stefan Stieglitz, Florian Brachten, Björn Ross
Research Group Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen
SM&S 2018, Copenhagen
@snurb_dot_info

2. @qutdmrc
Bots
● Bot types:
● Benign and legitimate
● Nefarious and subversive
● Political, commercial, …
● Exploiting affordances of social media platforms
● Main focus on Facebook and Twitter
● Bots as an industry:
● Fake followers
● Like, comment, share, retweet campaigns
● Trending topic generation
● URL promotion campaigns

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Bots on / for SoundCloud

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Bots on / for SoundCloud
● Native SoundCloud bots
● Plays, likes, comments, followers, etc.
● Addressed in Ross et al. (2018)
● SoundCloud-promoting Twitter bots:
● Sharing SoundCloud URLs, boosting SoundCloud metrics
● Can we detect such Twitter bots by their URL sharing patterns?

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Datasets
● Data:
● Tweets that share soundcloud.com URLs (after unshortening)
● Selection of tweets posted during March / April 2017
● 11.5m tweets posted by 2m Twitter accounts, sharing 2.1m tracks
● URLs standardised to soundcloud.com/[user name]/[track name]
● Top Tracks dataset:
● Tracks shared ≥ 1,000 times: 233 tracks, 914,131 tweets
● Top Accounts dataset:
● Accounts sharing tracks in ≥ 500 tweets: 649 accounts, 1m tweets

6. Preliminary Metrics

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Metrics 1: Account Diversity
● Account Diversity:
𝐴𝑐𝑐𝑜𝑢𝑛𝑡 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 =
# 𝑈𝑛𝑖𝑞𝑢𝑒 𝑇𝑤𝑖𝑡𝑡𝑒𝑟 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠
# 𝑇𝑤𝑒𝑒𝑡𝑠
● Calculated per track:
● ≈0: persistent promotion of track by small number of accounts
● ≈1: many accounts sharing the same track

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Metrics 2: Tweet Originality
● Tweet Originality:
𝑇𝑤𝑒𝑒𝑡 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑖𝑡𝑦 =
# 𝑁𝑜𝑛−𝑅𝑒𝑡𝑤𝑒𝑒𝑡𝑠 − # 𝑅𝑒𝑡𝑤𝑒𝑒𝑡𝑠
# 𝑇𝑤𝑒𝑒𝑡𝑠
● Calculated per track:
● –1: all tweets sharing the track are retweets
● +1: all tweets sharing the track are original tweets / @mentions
● Calculated per account:
● –1: all SoundCloud URL tweets by account are retweets
● +1: all SoundCloud URL tweets are original tweets / @mentions

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Metrics 3: Tweet Text Diversity
● Tweet Text Diversity:
𝑇𝑤𝑒𝑒𝑡 𝑇𝑒𝑥𝑡 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 =
# 𝑈𝑛𝑖𝑞𝑢𝑒 𝑇𝑤𝑒𝑒𝑡 𝑇𝑒𝑥𝑡𝑠
# 𝑇𝑤𝑒𝑒𝑡𝑠
● Calculated per track:
● ≈0: all tweets linking to SoundCloud track use identical text
● ≈1: all tweets linking to SoundCloud track use different texts
● Calculated per account:
● ≈0: all SoundCloud tweets posted by account use identical text
● ≈1: all SoundCloud tweets posted by account use different texts

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Metrics 4: SoundCloud URL Diversity
● SoundCloud URL Diversity:
𝑆𝑜𝑢𝑛𝑑𝐶𝑙𝑜𝑢𝑑 𝑈𝑅𝐿 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 =
# 𝑇𝑟𝑎𝑐𝑘𝑠 𝑆ℎ𝑎𝑟𝑒𝑑
# 𝑇𝑤𝑒𝑒𝑡𝑠
● Calculated per account:
● ≈0: all SoundCloud tweets by account share the same URL
● ≈1: all SoundCloud tweets by account share a different URL

11. Exploratory Analysis

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Analysis
● Preliminary analysis:
● Three per-track metrics:
● Account Diversity
● Tweet Originality
● Tweet Text Diversity
● Three per-account metrics:
● Tweet Originality
● Tweet Text Diversity
● SoundCloud URL Diversity
● Pairwise exploration of correlation patterns amongst metrics
● k-means clustering of tracks / accounts across each three metrics
● Abductive interpretation of patterns towards hypothesis formation

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Metrics per Track
retweeting
many
retweets by
few
accounts
many original
tweets by
few accounts
many original
tweets by few
accounts, using
many different
tweet texts

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Metrics per Accountretweets
originaltweets
many original
tracks, many
original texts
Few original
tracks, many
original texts
repeated sharing
but few retweets

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Observations
● Proposed metrics:
● Promising distinctions between tracks and accounts
● Need to further validate patterns through qualitative assessment
● Need to correlate between per-track and per-account metrics
● Need to explore patterns around SoundCloud users, not just tracks
● Further application:
● Test metrics on larger datasets
● Develop and test bot detection approaches on SoundCloud itself,
and compare findings with present study (Ross et al. 2018)

16. @qutdmrc
SM&S 2018, Copenhagen
@snurb_dot_info
Acknowledgments
This research is supported by the UA-DAAD project Emergent
Music Engagement Practices via SoundCloud and the ARC LIEF
project TrISMA: Tracking Infrastructure for Social Media Analysis.
Reference
B. Ross, F. Brachten, S. Stieglitz, P. Wikström, B. Moon, F.
Münch, and A. Bruns. 2018. Social Bots in a Commercial
Context – A Case Study on SoundCloud. Paper presented at
European Conference on Information Systems (ECIS),
Portsmouth, 23-28 July 2018.