  Online social media are key platforms for the public to discuss political
issues. As a result, researchers have used data from these platforms to analyze
public opinions and forecast election results. Recent studies reveal the
existence of inauthentic actors such as malicious social bots and trolls,
suggesting that not every message is a genuine expression from a legitimate
user. However, the prevalence of inauthentic activities in social data streams
is still unclear, making it difficult to gauge biases of analyses based on such
data. In this paper, we aim to close this gap using Twitter data from the 2018
U.S. midterm elections. Hyperactive accounts are over-represented in volume
samples. We compare their characteristics with those of randomly sampled
accounts and self-identified voters using a fast and low-cost heuristic. We
show that hyperactive accounts are more likely to exhibit various suspicious
behaviors and share low-credibility information compared to likely voters.
Random accounts are more similar to likely voters, although they have slightly
higher chances to display suspicious behaviors. Our work provides insights into
biased voter characterizations when using online observations, underlining the
importance of accounting for inauthentic actors in studies of political issues
based on social media data.
