Twitter as a social media has become increasingly used, including by its use by each Presidential candidate's stronghold to launch a campaign to influence prospective voters' electoral decisions in the 2019 Presidential Election in Indonesia. One strategy used in such a campaign on Twitter was by disseminating hashtags that were expected to become trending topics on Twitter. The dissemination of these hashtags aimed to build political rhetoric that can influence prospective voters' electoral decisions. Thus, this study sought to explore the patterns of hashtags disseminated by each candidate's stronghold to build political rhetoric and find out public sentiments in the posted Twitter contents. The number of tweets with #Jokowi2Periode and #2019GantiPresiden hashtags during the period of the 2019 Indonesian presidential and vice-presidential debates that were successfully downloaded using MAXQDA 18.1.1 software was 92,276. The research findings revealed that the distribution pattern of the #Jokowi2Periode hashtag tended to be more scattered (decentralized) by relying on the actor's presentation and the actor's speed in responding to tweets. In contrast, the spread of the #2019GantiPresiden hashtag was more centralized by relying on communication channels on Twitter. These two distribution patterns are discussed with the perspective of cyber psychology, through cuesfiltered-in and cues-filtered-out theories.

This study aims to assess the construct validity of the perceived risk scale on online transpor-tation users during the COVID-19 outbreak at once to test whether this scale can produce information that is not influenced by individual characteristics using Multiple Indicators and Multiple Cause (MIMIC) models. The 251 participants from different regions in Indonesia, such as East Java, Central Java, West Java, and outside Java participated in this study. The confirmatory factor analysis (CFA) approach was used as the main method in data analysis. The result showed that this six-aspect contextualized version of the scale was fit. The MIMIC model showed that this scale was not influenced by covariate variables, such as age, domicile, sex, marital status, length of time using online transportation, types of online transportation services, and occupation (except private employees). For further research, the exploratory study of risk perception and analysis of the roles of socioeconomic factors are suggested to do.