We analyze online collective evaluation processes through positive and negative votes in various social media. We find two modes of collective evaluations that stem from the existence of filter bubbles. Above a threshold of collective attention, negativity grows faster with positivity, as a sign of the burst of a filter bubble when information reaches beyond the local social context of a user. We analyze how collectively evaluated content can reach large social contexts and create polarization, showing that emotions expressed through text play a key role in collective evaluation processes.