The notions of Basking in Reflected Glory (BIRG) and Cutting Off Reflected Failure (CORF) have received a lot of attention in the psychology literature over many decades (Lachlan & Levy, 2016). BIRG is used to describe a person’s connection to a successful person or group. On the contrary, when a person with which we have been previously associated meets with failure, we may distance ourselves psychologically, thus CORF (Spinda, 2011). Very few studies have ever utilized big data analysis to test the notions above that emerged in political context, and those studies seems to only focus on the messages shown on Twitter (e.g., Lachlan, Levy & Xu, 2018). This study, however, tried to examine the messages on other Internet platforms such as Facebook, YouTube and Instagram, hoped to explore if the BIRG and CORF phenomenon occurred in the 2022 Taiwan’s local election. Two big data software, QSearch and OpView, were utilized. The Likes for the major candidates that were shown on Facebook one week before and after the election were explored and over 4,300,000 of which were collected. In addition, over 23,000 comments related to the major candidates of the election on Facebook, YouTube and Instagram were also gathered 24 hours leading up to and following the vote. Any Like and comment praising or supporting the major candidates after the election were considered as a BIRG behavior. In addition, we also checked whether the above Likes and comments changed significantly before and after the election. Results of the big data analysis showed that on Instagram the frequency of the comments which praised or supported the winner after the election was higher than the frequency of it before the election, which is 37.10% vs. 24.24%. And the frequency of the comments which praised or supported the loser after the election was lower than the frequency of it before the election, which is 23.6% vs. 52.7%. In other words, the BIRG and CORF phenomenon did occur to Instagram users. When comparing the frequency of the comments before and after the election on Facebook and YouTube, pro-loser comments showed lower frequency after the election. Nevertheless, pro-winner comments also showed lower frequency after the election. Besides, on Facebook, the number of Likes on the positive posts about the winner did not increase after the election (over 260,000 Likes) compared to the pre-election period (over one million Likes), the number of Likes on the positive posts about the loser was indeed lower after the election than before. The above results represented that the CORF phenomenon occurred to Facebook and YouTube users, however, the BIRG phenomenon did not occur to the users of those two Internet platforms. The possible reasons for these findings were discussed.

Keywords：Big-data analysis, BIRG, CORF, Instagram, Facebook, YouTube