Who Takes the Lead? Investigating the Reciprocal Relationship Between Organizational and News Agendas

Abstract

This study introduces the element of time to investigate the causal relation between organizational and news media agendas. Reciprocal time-series analyses were applied to daily-level aggregated press releases (n = 17,221) and news articles (n = 74,067). Results indicate that on the first level of agenda building, organizational and news agendas are intertwined in an intimate relation of reciprocal influence, in which organizations more often take the lead. Conversely, results suggest that on the second level of agenda building, organizational and news agendas influence each other less often. Organizational and newspaper characteristics proved useful to map the contingency of agenda-building effects. The findings suggest that organizational sources are more influential in the news discovery phase compared with the news-gathering phase, and imply that the unidirectional conceptualization of news media as a channel to vent organizational messages is too narrow.

How do nonprofit organizations (NPOs) effectively engage with the public on social media? Examining the effects of interactivity and emotion on Twitter

“…To measure emotion valence and emotion strength in the tweets, the textual information was further analyzed by the computer-assisted content analysis software SentiStrength (Thelwall et al, 2010). SentiStrength has been widely recognized as a state-of-art sentiment analysis tool to process short texts (Kroon and Van der Meer, 2021;Thelwall et al, 2010;Vargo et al, 2014). It has been rated among the bestautomated sentiment analysis approaches in terms of human-level accuracy (Kroon and Van der Meer, 2021;Vargo et al, 2014), especially for organizational messages (Kroon and Van der Meer, 2021).…”

Buffering Negative News: Individual-level Effects of Company Visibility, Tone, and Pre-existing Attitudes on Corporate Reputation

“…The SentiStrength algorithm is increasingly used in communication research (e.g., Vargo, Guo, McCombs, & Shaw, 2014) and has been shown to perform well compared with similar approaches (Gonçalves, Araújo, Benevenuto & Cha, 2013;González-Bailón & Paltoglou, 2015). The algorithm is also increasingly used in communication research on the effects of company news (e.g., Kroon & Van der Meer, 2018).…”

“…Because SentiStrength creates separate measures for negativity and positivity, articles can score either low or high on positivity and negativity. For example, a very neutral article may have values of 1 for positivity and −1 for negativity, whereas a very opinionated report that highlights different sides of an issue may score +3 and −4 or even +5 and −5 (see for a recent application and evaluation of this method Kroon & Van der Meer, 2018). In our news data, we obtain a Pearson correlation of −0.29 (p < .001) between positivity and negativity, indicating that, on average, articles are skewed toward either positivity or negativity rather than completely neutral.…”

“…Validation of Tone Measurement. In addition to previous studies validating SentiStrength (e.g., Thelwall et al, 2010) and showing that the algorithm performs well on news data about large corporations (e.g., Kroon & Van der Meer, 2018), we checked the validity of the SentiStrength measurement of positive and negative news against a hand-coded subsample of the news data used in the paper. To this end, we randomly selected a subset of 140 articles; 10 per outlet (n = 7) and for each of the two research periods.…”

To Pass or Not to Pass: How Corporate Characteristics Affect Corporate Visibility and Tone in Company News Coverage

“…Tone in the news was measured with the SentiStrength algorithm (Thelwall et al 2010), an algorithm that performs well (see, e.g. Vargo et al 2014;González-Bailón and Paltoglou 2015), also specifically with regard to company news (Kroon and Van der Meer 2018). The algorithm measures positivity and negativity in texts on a 5-point scale that ranges from −1 (not negative) to −5 (very negative) for negativity and 1 (not positive) to 5 (very positive) for positivity.…”