SOLO: A Corpus of Tweets for Examining the State of Being Alone

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 1567–1577 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 1567 SOLO: A Corpus of Tweets for Examining the State of Being Alone Svetlana Kiritchenko1, Will E. Hipson2, Robert J. Coplan2, Saif M. Mohammad1 1National Research Council Canada, 2Carleton University, Ottawa, Canada svetlana.kiritchenko@nrc-cnrc.gc.ca, williamhipson@cmail.carleton.ca, robert.coplan@carleton.ca, saif.mohammad@nrc-cnrc.gc.ca Abstract 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. Keywords: solitude, lonely, mental health, well-being 1. Introduction We have all experienced the state of being alone one time or another: perhaps, a loved one was away, or our In- stagram post did not stir up a barrage of likes, or we enjoyed a quiet hike, or we felt disconnected from those around us. Further, older people and young adults experience loneli- ness at markedly higher rates than others (Luhmann and Hawkley, 2016; Hawkley and Capitanio, 2015). The state of being alone can have a substantial impact on our lives. On the one hand, loneliness—a negative and unwanted state of being alone—has been shown to be cor- related with increased cognitive decline, dementia, depres- sion, suicide ideation, self harm, and even death (Gerst- Emerson and Jayawardhana, 2015; Hawkley and Capitanio, 2015; Luo et al., 2012; Endo et al., 2017).1 On the other hand, solitude—a positive and self-driven state of being alone—has been shown to improve autonomy, creativity, and well-being (Long et al., 2003; Knafo, 2012; Coplan and Bowker, 2017; Coplan et al., 2019a). Loneliness and solitude have also been shown to play a role in the adaptive fitness of our species (Hawkley and Capitanio, 2015; Lar- son, 1990). Thus loneliness and solitude are starting to re- ceive substantial amounts of attention from the medical and psychological research. Yet, there is no large-scale compu- tational work on analyzing the language of being alone. Here, for the first …

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