(2024)

Abstract: The rise of sensationalism in news reporting, 001 driven by market saturation and online compe002 tition, has compromised news quality and trust. 003 At the core of sensationalism is the evocation of 004 affective responses in the readers. Current NLP 005 approaches to emotion detection often over006 look the subjective differences in groups and 007 individuals, relying on aggregation techniques 008 that can obscure nuanced reactions. We intro009 duce a novel large-scale dataset capturing sub010 jective affective responses to news headlines. 011 The dataset includes Facebook post screenshots 012 from popular UK media outlets and uses a 013 comprehensive annotation scheme. Annota014 tors report their affective responses, provide 015 discrete emotion labels, assess relevance to cur016 rent events, and indicate sharing likelihood. Ad017 ditionally, we collect demographic, personality, 018 and media consumption data. This ongoing 019 dataset aims to enable more accurate models 020 of affective response by considering individual 021 and contextual factors. This work is ongoing 022 and we highly appreciate any feedback. 023

text: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 555–563 August 11-16, 2024 ©2024 Association for Computational Linguistics
The rise of sensationalism in news reporting,001 driven by market saturation and online compe-002 tition, has compromised news quality and trust.003 At the core of sensationalism is the evocation of004 affective responses in the readers. Current NLP005 approaches to emotion detection often over-006 look the subjective differences in groups and007 individuals, relying on aggregation techniques008 that can obscure nuanced reactions. We intro-009 duce a novel large-scale dataset capturing sub-010 jective affective responses to news headlines.011 The dataset includes Facebook post screenshots012 from popular UK media outlets and uses a013 comprehensive annotation scheme. Annota-014 tors report their affective responses, provide015 discrete emotion labels, assess relevance to cur-016 rent events, and indicate sharing likelihood. Ad-017 ditionally, we collect demographic, personality,018 and media consumption data. This ongoing019 dataset aims to enable more accurate models020 of affective response by considering individual021 and contextual factors. This work is ongoing022 and we highly appreciate any feedback.023
1 Introduction024
The saturation of the traditional media market and025 increased competition in the online space have led026 to a rise in sensationalism in news reporting, ap-027 pealing to readers’ emotions to maximize click rate028 and sharing online (Kleemans and Hendriks Vette-029 hen, 2009). This leads to a deterioration of news030 quality (Wang, 2012), a distorted perception of the031 state of the world among the public (Boyer, 2023),032 and declining trust in the news industry (Kleemans033 et al., 2017).034 While often framed as an objective character-035 istic of news content and form (Kleemans and036 Hendriks Vettehen, 2009; Arbaoui et al., 2020),037 sensationalism is fundamentally about eliciting an038 affective response from the audience. This inher-039 ent subjectivity, akin to other psychological con-040 cepts, is influenced by a complex interplay of in-041
dividual and group-level factors. Research on dif- 042 ferential media effects demonstrates how diverse 043 audiences, shaped by factors such as demograph- 044 ics, personality traits, and cultural backgrounds, 045 respond to media content in distinct ways (Oliver, 046 2002; Valkenburg and Peter, 2013; Soroka et al., 047 2019). This variability in affective responses is 048 further supported by emotion research highlighting 049 the significant influence of individual character- 050 istics like age, gender, and personality, alongside 051 group-level variables like culture, on everyday emo- 052 tional experiences (Kring and Gordon, 1998; Costa 053 and McCrae, 2008; Charles and Carstensen, 2010; 054 Mesquita and Frijda, 1992). Therefore, assessing 055 sensationalism solely based on content analysis on 056 the emotion used in the news, without accounting 057 for the audience’s subjective experience and indi- 058 vidual differences, risks a simplistic and potentially 059 inaccurate understanding of the phenomenon. 060
Numerous NLP studies aim to measure emotion 061 in text, yet many fail to explicitly consider the per- 062 spective of the analysis (e.g., writer vs. reader) 063 and rely on aggregation techniques like majority 064 voting or averaging for annotation labels. How- 065 ever, research on subjectivity in NLP annotations, 066 emphasizes the inherent subjectivity of these con- 067 structs (Ovesdotter Alm, 2011; Plank, 2022; Cab- 068 itza et al., 2023). Aggregating subjective responses 069 without acknowledging individual variability and 070 potential biases in perception risks obscuring nu- 071 anced emotional reactions and generating poten- 072 tially misleading conclusions. 073
To address these limitations, we introduce a 074 novel large-scale dataset focused on capturing the 075 inherent subjectivity of affective responses to news 076 content. Our dataset consists of screenshots from 077 publicly available Facebook posts by the most pop- 078 ular UK media outlets (see Appendix for a full list). 079 We employ a multi-faceted annotation scheme, re- 080 quiring annotators to: (1) report their affective re- 081 sponse using the valence-dominance-arousal frame- 082
1 555
work (Mehrabian and Russell, 1974), (2) provide083 discrete emotion annotations based on Plutchik’s084 eight basic emotions (Plutchik, 1980), (3) assess085 the relevance of the post to current events, and086 (4) indicate their likelihood of sharing the post.087 Furthermore, we collect a comprehensive set of088 covariates for each annotator, encompassing demo-089 graphic information, personality traits, and media090 consumption habits. This rich dataset will enable091 the development of more nuanced and accurate092 models of affective response to news, taking into093 account both individual differences and contextual094 factors. This dataset collection effort is still ungo-095 ing.096
2 Related Works097
2.1 News and Emotion098
While news content often leans negative, eliciting099 negative emotions and heightened arousal in read-100 ers (Soroka et al., 2019), individual responses can101 vary significantly based on demographics, person-102 ality, and other background factors (Oliver, 2002;103 Valkenburg and Peter, 2013; Soroka et al., 2019).104 This is crucial as emotional reactions to news can105 profoundly influence perception, cognition, and be-106 havior. Affect, for instance, provides evaluative107 feedback on one’s thoughts and inclinations, shap-108 ing reasoning and decision-making (Storbeck and109 Clore, 2008)]. Existing research on news percep-110 tion predominantly focuses on the emotional tone111 of the news itself, rather than the emotions evoked112 in individual readers (de Hoog and Verboon, 2020).113 To address this gap, this work shifts perspective114 and introduces a large-scale dataset designed to an-115 alyze how diverse individuals emotionally respond116 to different news headlines.117
2.2 Emotion Detection in NLP118
Emotion detection has been a core task in NLP119 for nearly two decades (Strapparava and Mihalcea,120 2007). Recent years have seen a large number of121 valuable resources on the task (see Demszky et al.122 (2020); Oberländer et al. (2020) for a overview).123 These efforts have significantly advanced the field,124 leading to more accurate and robust emotion detec-125 tion systems.126 However, most existing datasets rely on aggre-127 gated “gold labels”, overlooking the inherent sub-128 jectivity and variation in human emotional percep-129 tion (Ovesdotter Alm, 2011; Plank, 2022; Cabitza130 et al., 2023). Ample research demonstrates the im-131
pact of both individual characteristics (e.g., age, 132 gender, personality) and group-level factors (e.g., 133 culture) on how we perceive and interpret emo- 134 tions (Kring and Gordon, 1998; Costa and McCrae, 135 2008; Charles and Carstensen, 2010; Mesquita and 136 Frijda, 1992), most existing datasets rely on aggre- 137 gated "gold labels." This approach, while simplify- 138 ing annotation, overlooks the genuine variation and 139 subjectivity inherent in human emotional responses 140 (Ovesdotter Alm, 2011; Plank, 2022; Cabitza et al., 141 2023). Consequently, models trained on such data 142 may struggle to capture the nuanced ways in which 143 emotions are expressed and understood. 144
Limited attempts have been made to incorporate 145 annotator information. For instance, Diaz et al. 146 (2018) provides demographic data alongside senti- 147 ment annotations. However, this dataset only con- 148 tains sentiment annotation, is restricted to a specific 149 online community, and is thus unsuitable for our 150 purpose. 151
3 Dataset Collection Protocol 152
Recognizing the limitations of existing emotion de- 153 tection datasets, we develop a novel data collection 154 protocol aimed at capturing individualized affective 155 response to news headlines. 156
We first collect a selection Facebook news posts 157 from a list of major UK news outlets from April 158 1 to April 20, 2024, using CrowdTangle. While 159 acknowledging that social media content may not 160 fully represent the entirety of a news outlet’s output, 161 we posit that the posts chosen for these platforms 162 reflect the outlets’ editorial decisions and public 163 image. Typically, these posts consist of an image, a 164 short description, and the headline, with the image 165 linking to the full news article. An example can be 166 seen in Figure 3. To ensure ecological validity and 167 minimize bias, we took screenshots of the news 168 posts, capturing the reaction counts while any com- 169 ment information. These screenshots were then 170 presented to the annotators. 171
We recruit our annotators from Prolific. We have 172 around 5 annotators for each headlines. We make 173 sure of features such as stratified sampling to en- 174 sure a balanced set of annotators in terms of gender, 175 age and political learning. In total, each annotators 176 annotator around 50 headlines and the two stage 177 combined take around 45 minutes. We therefore 178 pay the annotators £8.58, in accordance with the 179 National Living Wage. 180
Our annotation process involved two stages: 181
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Stage 1: Covariate Collection182 In this initial stage (implemented in Qualtrics), we183 gather essential background information (which we184 will refer to as persona variables henceforth) about185 annotators. This includes:186
• Demographics (age, gender, education, in-187 come level etc.)188
• Ideology189
• Questions about news consumption habits190 (e.g. How often do you fact-check news sto-191 ries you come across; Which of the following192 platforms do you use for news nowadays)193
• Trust in major UK news outlets: To gauge how194 trust in news sources (and hence as a proxy of195 consumption) might affect perception196
• A short version of the Cognitive Reflection197 Test (Frederick, 2005): to measure the ten-198 dency to engage in reflective thinking versus199 intuitive thinking200
• The Ten-Item Personality Measure (Gosling201 et al., 2003): To capture basic personality202 traits that may influence annotation behavior203
• Selected questions from the Perth Emotional204 Reactivity Scale (Preece et al., 2018): To as-205 sess emotional reactivity which could affect206 judgment.207
• Selected questions from the Positive and Neg-208 ative Affect Schedule (Crawford and Henry,209 2004): To evaluate the annotators’ current af-210 fective state and its potential influence on their211 annotations.212
We also present the annotation guideline1, which213 are adapted from the seminal work of Bradley and214 Lang (2007), to the annotators at this stage but they215 always have access to it in the second stage as well.216
Stage 2: Headline Annotation217 We then present the screenshots to the annotators218 with a website built on top of the the Potato an-219 notation tool (Pei et al., 2022). For each screen-220 shot, we ask the annotators to rate the valence,221 arousal and dominance they feel after reading222 the headline using the validated Self-Assessment223 Manikin (Bradley and Lang, 1994). We also ask the224
1https://docs.google.com/document/d/ 1RPkjaPSksRbCy3y5d4WltidcUGhlH_np-aAuY2eH33c/
annotators to rate the discrete emotion categories 225 based on Plutchik’s eight basic emotions (Plutchik, 226 1980). This is because existing work have been 227 using both and we would like to have a dataset that 228 is comparable to either. We also ask the annotators 229 the following three additional questions: 230
1. When considering your emotional reaction to 231 this Facebook post, which element do you feel 232 has the most influence? 233
2. Considering your personal experiences, inter- 234 ests, and the context of your life, how relevant 235 do you find the following headline? Please se- 236 lect the option that best reflects your opinion. 237
3. Imagine you are seeing this headline for the 238 first time on social media. How likely are you 239 to share this news with others (e.g., through 240 social media, messaging apps, or in person)? 241 Please select the option that best reflects your 242 opinion. 243
4 Preliminary Results 244
We have annotated 1,102 instances using a total 245 of 113 annotators, averaging 5.27 annotations per 246 sample. 247
Distribution of Annotators We show the distri- 248 bution of our annotators among key persona vari- 249 ables in Table 1. Our data has a broad coverage in 250 terms of the key persona variables listed. 251
Distribution of Annotations We present the dis- 252 tribution of the annotation variables we collect for 253 each headline in Figure 1. In Figure 1a, 1b,and 1c, 254 we observe that the neutral value of 4 is the most 255 common for valence, arousal and dominance. As 256 anticipated, the valence scores tend to skew nega- 257 tively, arousal scores are predominantly high, and 258 dominance scores skew slightly low. 259
For discrete emotions (Figure 1d), “neutral” is 260 the most commonly selected emotion, followed by 261 “sad”. Interestingly, the next most frequent emotion 262 is "happy," which is likely due to the limitation of 263 having only one category for positive emotions. 264
Regarding relevance (Figure 1e), almost half of 265 the annotations (44%) indicate “Not at all” relevant, 266 with only 3.8% marked as “extremely relevant.” 267 For sharing inclination (Figure 1f), the distribution 268 is even more skewed, with 54.5% of the annotations 269 indicating “very unlikely” to share. 270
The majority of annotations (52.3%, Figure 1g) 271 reveal that both the text and image significantly 272
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influence emotional reactions to news headlines. In273 contrast, approximately a third (36.7%) highlight274 the text alone as the primary factor. This indicates275 the importance of considering both the image and276 the text when modeling affective responses to news277 headlines on social media, rather than focusing278 solely on one or the other.279
Relationship Between Arousal and Valence280 "Figure 2 depicts the average valence and arousal281 scores per headline, revealing a V-shaped distribu-282 tion. This pattern, characterized by high arousal283 at both low and high valence levels, aligns with284 previous findings [Lang1997, Kurdi2017]. How-285 ever, our results differ from those of [Kurdi2017]286 in exhibiting a greater concentration of data points287 at higher arousal levels (above 6, particularly in288 the second quadrant, which corresponds to low va-289 lence and high arousal). This discrepancy may be290 attributed to the inherent negativity bias prevalent291 in news headlines, as compared to the more diverse292 range of scenes and objects typically included in293 image-based studies."294
We calculate the average valence and arousal295 for each headline and present the results in Fig-296 ure 2. The distribution follows a V-shaped pat-297 tern, where arousal levels are high at both low and298 high extremes of valence, consistent with prior re-299 search (Lang et al., 1997; Kurdi et al., 2017). No-300 tably, our data diverges somewhat from the find-301 ings of Kurdi et al. (2017), displaying a higher302 concentration of points at elevated arousal levels303 (above 6) in both the first and second quadrants.304 This trend is particularly pronounced in the second305
quadrant, characterized by very low valence and 306 very high arousal. We hypothesize that this dis- 307 crepancy arises from the inherently negative nature 308 of news headlines, in contrast to the more varied 309 emotional content typically found in datasets com- 310 prising images of scenes and objects. 311
Group Level Differences We show the group- 312 level mean and standard deviation of the valence 313 and arousal annotation in Table 1. 314
Men exhibited a slightly higher mean valence 315 (Mean (V) = 3.62) compared to women (Mean (V) 316 = 3.38). Conversely, women showed a higher mean 317 arousal (Mean (A) = 4.32) compared to men (Mean 318 (A) = 4.07). 319
Left-leaning participants reported the lowest 320 mean valence (Mean (V) = 3.32) and the highest 321 variability in arousal (Std (A) = 1.54). 322
A particularly notable finding is within the neu- 323 roticism variable. Annotators with high neuroti- 324 cism had a significantly higher mean arousal (Mean 325 (A) = 4.60), consistent with well-documented asso- 326 ciations between neuroticism and higher emotional 327 reactivity (Costa and McCrae, 1980). 328
There is a large different in the group-level mean 329 in annotators with different levels of current af- 330 fective state (PANAS Positive - Panas Negative). 331 The mean arousal score ranges from 4.44 to 4.18 332 to 3.93 from the lowest to highest level of current 333 affective state. Also interestingly, annotators with 334 the lowest current affective state report the lowest 335 standard devitation in arousal level. This is despite 336 the standard deviation of arousal level being largely 337 the same in any other groupings. 338
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We further conduct a fixed effect linear regres-339 sion analysis2, including all the persona variables340 mentioned in Table 1. The effect of gender, politi-341 cal leaning, neuroticism and current affective state342 are significant (p<0.05).343
Conclusion and Future Work In this paper, we344 describe an ongoing project to collect a large-scale345 individualized affective news response dataset, en-346 riched with various persona variables about indi-347 vidual annotators. We envision this dataset to be348 useful for multiple purposes, for both psychology349 and natural language processing. For example, it350 could be helpful for understanding the group-level351 and individual-level covariates that would be im-352 portant to explain the varied affective response to353 news headlines and the underling mechanism that354 leads to such differences. It could be valuable for355 NLP researchers focused on developing culturally-356 aware, pluralistic systems that account for global357 diversity in human responses. The dataset also has358 the potential to facilitate the creation of algorithms359 designed to accommodate individual differences,360 paving the way for personalized language models361 that could greatly enhance applications like per-362 sonal assistants. As this project is still in progress,363 we highly welcome any feedback.364
2In R notation, annotation ∼ persona variables
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A Appendix 509
The list of news outlets that we sample from in- 510 clude: 511
• Daily Mail 512
• The Telegraph 513
• The Mirror 514
• Metro 515
• The Sun 516
• Daily Star 517
• The Independent 518
• Daily Express 519
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• The i Paper520
• GB News521
• LADbible522
• The Economist523
• The Times and The Sunday Times524
• The Guardian525
• ITV News526
• BBC News527
• Sky News528
• Reuters UK529
• LBC530
• Financial Times531
• Channel 4 News532
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heading: Future Work