发现推广类社交机器人随时间演变行为模式，对检测系统提出新挑战。
Bots Don't Sit Still: A Longitudinal Study of Bot Behaviour Change, Temporal Drift, and Feature-Structure Evolution
- 分析2615个推广账号的280万条推文，发现其行为特征随时间非平稳变化
- 九类元特征逐年上升，语言多样性下降，长期账户更擅用表情符号和多样化表达
- 不同代际机器人行为组合更复杂，检测系统需考虑动态演化机制
社交机器人在在线平台中广泛用于推广、说服与操纵。现有检测系统多将行为特征视为静态，隐含假设机器人行为不随时间改变。本文针对推广类推特机器人，分析个体行为信号及其相互关系的演变。基于2615个推广账号及280万条推文，构建了十类内容型元特征的年度时间序列。增广迪基-福勒（ADF）与KPSS检验结合线性趋势分析显示，所有十项均非平稳：其中九项随时间上升，语言多样性轻微下降。按激活代际与账户年龄分层显示：第二代机器人最活跃且链接密集；短生命周期机器人表现出高强度重复行为，大量使用话题标签与网址；长期账户活动较少但语言更丰富，表情符号使用更多样。进一步分析18个可解释二值特征（涵盖行为、主题相似性、网址、话题标签、情感、表情符号与媒体）间的共现关系（共153对），卡方检验表明几乎所有配对均存在依赖。斯皮尔曼相关系数强度与符号发生转变：多数关联（如多个话题标签搭配媒体、情感与网址）增强，部分则由弱正转为弱或中度负相关。后期代际表现出更结构化的线索组合。研究结果表明，推广型社交机器人在个体特征与特征间依赖关系层面均随时间适应演化，对基于历史行为特征训练的检测系统设计与评估具有直接启示。
原文摘要 · Abstract (English)
Social bots are now deeply embedded in online platforms for promotion, persuasion, and manipulation. Most bot-detection systems still treat behavioural features as static, implicitly assuming bots behave stationarily over time. We test that assumption for promotional Twitter bots, analysing change in both individual behavioural signals and the relationships between them. Using 2,615 promotional bot accounts and 2.8M tweets, we build yearly time series for ten content-based meta-features. Augmented Dickey-Fuller and KPSS tests plus linear trends show all ten are non-stationary: nine increase over time, while language diversity declines slightly. Stratifying by activation generation and account age reveals systematic differences: second-generation bots are most active and link-heavy; short-lived bots show intense, repetitive activity with heavy hashtag/URL use; long-lived bots are less active but more linguistically diverse and use emojis more variably. We then analyse co-occurrence across generations using 18 interpretable binary features spanning actions, topic similarity, URLs, hashtags, sentiment, emojis, and media (153 pairs). Chi-square tests indicate almost all pairs are dependent. Spearman correlations shift in strength and sometimes polarity: many links (e.g. multiple hashtags with media; sentiment with URLs) strengthen, while others flip from weakly positive to weakly or moderately negative. Later generations show more structured combinations of cues. Taken together, these studies provide evidence that promotional social bots adapt over time at both the level of individual meta-features and the level of feature interdependencies, with direct implications for the design and evaluation of bot-detection systems trained on historical behavioural features.
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