Discover more from Rozado’s Visual Analytics
Pessimism in News Media Headlines
In previous work, I documented the growing emotional negativity (anger, fear, sadness, etc) of American news media headlines between the years 2000 and 2019. Here, I extend that work by examining the attitudinal tone (pessimism, optimism or neutrality) of news media headlines over a longer timeframe: 1950-2022.1
I use ChatGPT to automatically label a longitudinal sample of 1.7 million headlines from 12 popular U.S. news media outlets as to whether they convey optimism, pessimism, or neutrality. The results show an overall increasing proportion of headlines conveying pessimism since the 1950s. A period of substantial increase in pessimistic headlines is apparent during the late 1960s and early 1970s. A subsequent period of stabilization occurs during the mid-1970s and through the 1980s and 1990s. Since the turn of the century, the trend of growing pessimism in news media headlines has resumed.
The increasing proportion of headlines conveying pessimism has mostly come at the expense of attitudinal neutral headlines.
In contrast, optimistic headlines display a relatively flat trend for the period studied. Although a spike of optimistic headlines during the 1990s is apparent in the figure below.
The biggest methodological limitations of the analysis above is the unequal temporal coverage of outlets in the sample. That is, the sample of outlets becomes sparse the further we go back in time. From the 1950s until the 1970s, the analyzed data set only contains headlines from the New York Times. Headline data for the Washington Post, Los Angeles Times and the Wall Street Journal begins in the 1970s, 1980s and 1990s respectively.
Is it reasonable to use attitudinal pessimism in New York Times headlines as a proxy for overall media attitudinal pessimism from the 1950s to the 1970s? That’s debatable. The correlation between headlines’ pessimism in the New York Times time series and the average pessimism of headlines in all the other outlets analyzed is 0.69. Most proxy measurements are often imperfect to an extent. But given the correlation given above, perhaps it is not too unreasonable to assume that New York Times attitudinal pessimism in headlines from the 1950s to the 1970s can act as an approximate gauge of US news media attitudinal pessimism at the time.
Why has the proportion of pessimistic headlines in news media increased over the past decades? Since the 1950s, we have witnessed a heterogeneous set of social trends that can both justify pessimism and/or optimism about the present and the future. Extraordinary technological advancements with obvious implications for productivity increases, wealth creation, health improvements and access to knowledge have taken place since the 1950s: microprocessors, Internet, smartphones, electric cars, CRISPR and more recently, AI, just to name a few. Simultaneously, since the 1950s several longitudinal trends are supportive of optimism about the present and future: worldwide poverty reduction, declining child labor, decreasing child mortality, increasing leisure time and increasing disposable income.2 However, a lesser rosy picture emerges (at least in many Western countries) if we take into account simultaneous trends manifesting decreasing social trust, decreasing fertility, decreasing social ties, decreasing pair bonding, increasing loneliness, worsening mental health3, increasing political polarization, increasing suicide rates (in the U.S. at least) and dysfunctional institutions. Hence, there are concurrent reasons to feel both optimistic and pessimistic about the present and the future. For some reason, American news media has focused on increasingly emphasizing pessimism over optimism or attitudinal neutrality in their headlines since the 1950s (with an almost 30 year stable period from the 1970s to the 1990s). Elucidating the reasons for such a trend in news media and its impact, or lack thereof, on the wider society deserves further investigation.
5,000 random headlines per outlet and year. Automated labelling using ChatGPT.