SOLO: A Corpus of Tweets for Examining the State of Being Alone
This provides a computational analysis of psychological concepts in social media data, which is incremental as it applies existing methods to new data.
The researchers tackled the problem of understanding how terms related to being alone are used in online language by creating SOLO, a corpus of over 4 million tweets, and found that 'solitude' co-occurs with positive words while 'lonely' and 'loneliness' co-occur with negative words, confirming psychological distinctions, with women more likely to report negative feelings and teenagers more likely to use 'lonely'.
The state of being alone can have a substantial impact on our lives, though experiences with time alone diverge significantly among individuals. Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept of loneliness, a negative state of dissatisfaction with the quality of one's social interactions. Here, for the first time, we conduct a large-scale computational analysis to explore how the terms associated with the state of being alone are used in online language. We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms 'solitude', 'lonely', and 'loneliness'. We use SOLO to analyze the language and emotions associated with the state of being alone. We show that the term 'solitude' tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms 'lonely' and 'loneliness' frequently co-occur with negative, low-dominance words (e.g., scared, depressed), which confirms the conceptual distinctions made in psychology. We also show that women are more likely to report on negative feelings of being lonely as compared to men, and there are more teenagers among the tweeters that use the word 'lonely' than among the tweeters that use the word 'solitude'.