Reports of personal experiences or stories can play a crucial role in argumentation, as they represent an immediate and (often) relatable way to back up one's position with respect to a given topic. They are easy to understand and increase empathy: this makes them powerful in argumentation. The impact of personal reports and stories in argumentation has been studied in the Social Sciences, but it is still largely underexplored in NLP. Our work is the first step towards filling this gap: our goal is to develop robust classifiers to identify documents containing personal experiences and reports. The main challenge is the scarcity of annotated data: our solution is to leverage existing annotations to be able to scale-up the analysis. Our contribution is two-fold. First, we conduct a set of in-domain and cross-domain experiments involving three datasets (two from Argument Mining, one from the Social Sci-ences), modeling architectures, training setups and fine-tuning options tailored to the involved domains. We show that despite the differences among datasets and annotations, robust cross-domain classification is possible. Second, we employ linear regression for performance mining, identifying performance trends both for overall classification performance and individual classifier predictions. Although personal narratives and experiences naturally fill an important place in our everyday discussions, they still do not conform to the classic ideal of a good argument. A good argument contains facts and logical conclusions, but as soon as more personal or emotional nuances are involved, it deviates from the norm. According to the theory of Deliberative Democracy (Habermas, 1996; Fishkin, 1995), the discourse that precedes political decisions plays a central role. Here, too, until the so-called affective turn in Social Sciences (Hoggett and Thompson, 2012), the assumption was that an exchange of arguments that is as rational as possible can lead to better political decisions. Recent studies in Deliberative Theory, however, are increasingly concerned with the interplay between classical argumentation and alternative forms of argumentation , which include personal experiences, narratives and emotions, and their positive effects on discourse (Polletta and Lee, 2006; Esau, 2018; Gerber et al., 2018; Maia et al., 2020). A norm that only allows rational and logical argumentation firstly does not correspond to human realistic communication and secondly bears the danger that less educated groups or groups in which other communication standards prevail are marginalized. Arguments with personal experiences are therefore important to fulfill one of the core deliberative standards, namely inclusivity (Polletta and Gardner, 2018). The following example is taken from a discussion about regulations to ban peanut products on airlines and illustrates further possible positive effects of arguments with personal experiences: My daughter has been tested 4 times for her allergy to peanuts. She is in the highest category of reactivity which means if peanuts are being ingested in her vicinity, she could die. A buffer zone simply doesn't work in a confined space such as an airline. 1 The perspective of the frightened parent and their allergy-affected daughter is likely to elicit emphatic reactions from the other participants in the discussion and illustrates the rationale of proponents of a peanut ban. Our work represents the first step towards identifying these arguments at a large scale by developing models for automatic detection of contributions with personal experiences and stories (to which we refer with the general label of reports in the paper). This is the necessary first step to be able to further examine the role of such arguments in 1 The comment was taken from the e-rulemaking platform regulationroom.org. reasoning and their relationship with Argument Quality (Wachsmuth et al., 2017a) and Discourse Quality (Steenbergen et al., 2003), To tackle this task we collect available datasets and investigate the best strategy to merge the available sources. In two of the datasets we employ, produced by the Argument Mining community (Reg-ulation Room and Change My View) our targeted phenomenon is annotated as testimony . In the third dataset, from the Social Science community (Eu-ropolis) it is annotated as storytelling . The two categories are obviously not fully overlapping, but we hypothesize that they share a conceptual core which can be leveraged for robust classification. We perform a large set of in-domain, out-domain and cross-domain experiments including different domain-adaptation strategies for the pre-trained language models used in the classification. We analyze our results using performance mining and show that the cross-domain training setup leads to the most robust results and also has the most positive effect when used with a domain-adapted LM. We also conduct regression-based error analysis and compare the most salient features of the two largest datasets that get picked up by the most robust model and show that prototypical textual properties of reports can push the model in the right direction (probability of reports close to decision boundary) but can also lead to over-generalization (higher probability of reports for false positives). Argument Mining & NLP The automatic detection of arguments with personal experiences was first tackled by Park and Cardie (2014) with the goal to classify claims as verifiable or unverifiable and to be able to detect what type of evidence would be necessary as a consequence. They defined the subcategory verifiable experiential for verifiable claims that contain personal experiences. Their dataset contains comments from RegulationRoom, 2 an e-rulemaking platform with the goal of enabling online deliberation: governmental institutions or companies can have their proposals about new regulations discussed by citizens to get feedback. Park and Cardie (2014) conducted classification experiments with a SVM using different feature sets hypothesizing that the amount of past tense and first personal pronouns would be most predictive for verifiable experientials. They 2 http://regulationroom.org/ achieved a F1-score of  70% on the RegulationRoom dataset. This work was further extended in Park et al. (2015b) and reformulated as a sequence classification task. Park et al. (2015a) and Park and Cardie (2018) developed a new annotation scheme targeted at elementary units in arguments. This schema introduces testimony as an elementary unit which corresponds to a proposition about the author's personal state or experience. A similar evidence type, called anecdote and defined as a personal experience of the author or a narration of a concrete example or event was classified in news editorials (Al-Khatib et al., 2016, 2017). Song et al. (2016) build a database for claims and suitable anecdotes focusing on stories with a clear narrative structure and popular main characters (e.g. the Dalai Lama). Wang et al. (2019) model different persuasion strategies in dialogues targeting social good (e.g. fund raising): personal stories which exemplify positive outcomes and benefits of a donation are one of these strategies. Social Sciences The role of personal narratives in digital and deliberative democracy has gained more attention in the recent years. Polletta and Lee (2006) were the first who investigated the role of personal narratives in online argumentation. Their data was further analyzed in Black (2008) and Black (2013), who emphasized the importance of personal narratives in discussions for forming group identity and understanding oth-ers' perspectives. The relationship between storytelling and emotions was investigated in Esau (2018) who pointed out that these are especially useful when discussing social problems that cannot be addressed with factual information alone. Maia et al. (2020) annotated the functions of storytelling (e.g. do people tell an experience as a disclosure of harm or to propose a solution?) and examined how the different types of narratives effect the quality of a discussion. As far as available annotation is concerned, we conduct our experiments on the Europolis corpus (Gerber et al., 2018). Europolis contains spoken contributions from a transnational poll, in which citizens from different European countries got together to discuss about the EU and the topic immigration. The spoken contribution have been annotated with different aspects of deliberative quality (e.g. does the speaker show respect or value other participants?), and one of these aspects includes alternative forms of communication. Relevant to our work is the annotation category 5531 storytelling , marking those contributions which contain personal experiences or concrete examples of a speaker's own country. Regulation Room ( RegRoom) Our experiments are based on the final version of the Cornell eRulemaking Corpus (CDCP) (Park and Cardie, 2018) It contains 725 comments from Regulation Room, discussing consumer debt collection practices in the United States. The comments are annotated with different proposition types, and our category of interest is testimony . Change My View ( CMV ) contains 344 comments from the subreddit ChangeMyView 3 (Egawa et al., 2019) and is annotated with a similar schema as introduced in Park et al. (2015a). Our reference category is testimony . Europolis (Gerber et al., 2018), already introduced in section 2, contains a total of 856 transcribed speech contributions whose original language was German, French, and Polish (only available in the English translation). 4 Europolis is annotated along many deliberative quality dimensions (Steenbergen et al., 2003), and our reference category is storytelling . From now on, we will use the neutral term REPORT as our positive label, of which testimony and storytelling are dataset-specific declinations. Note that, while storytelling is annotated at the document level in Europolis, RegRoom and CMV contain span-level annotation of testimony : for our experiments, a document containing a testimony span is considered as a positive instance of REPORT . For all datasets, reports are the minority class (Re-gRoom: 41%; CMV: 37%, Europolis: 35%). Table 1 displays one example per dataset. In the example of Europolis the participant describes the general situation in their country in a more objective manner, thus reflecting a quite broad definition of a personal narrative. The RegRoom example is very personal and emotional in its tone, displaying more prototypical features of a personal experience. The CMV example is somewhere in between: it 3 On CMV, users exchange views on a variety of different topics and can reward other comments if they are convincing or have led to a change of their opinion. Research on persuasion on CMV has targeted, for example, interaction dynamics and stylistic choices (Tan et al., 2016) or the effect of social pressure (Jain and Srivastava, 2021). 4 We translated the German and French transcriptions into English using DeepL and used the professional English translation of the Polish data. The datasets were preprocessed by removing time stamps and URLs. With freely available tools, we extracted a total of 51 features (henceforth, contribution-level features) from four categories: 5 Surface features (6 features), e.g., length in tokens; average amount of characters and syllables per word. We hypothesize that longer comments are more likely to contain reports (verbose retelling of concrete stories / examples). Syntactic features (6), e.g., relative amount of fine-grained part-of-speech tags per comment (e.g., personal pronouns, past tense, auxiliaries, named enti-ties). Specific categories, e.g. first-person pronouns and past tense verbs ("I had an unpleasant experi-ence..."), are likely to be predictive of reports. Textual complexity (19), with different measures of lexical diversity, lexical sophistication and readability. While prototypical reports are expected to exhibit lower textual complexity (character repetitions, more concrete concepts), the modulation of complexity in the reports in our datasets is an open question. Sentiment/Polarity (20), e.g. amount of positive or negative adjectives/nouns, amount of specific emotions (joy, fear). We hypothesize that comments with reports will have more marked polarity. Task We perform binary classification at the document level: forum posts in RegRoom and CMV, spoken contributions in Europolis. We create 10 random train / dev / test splits using 15 % as development and 20 % as test data and we ensure that every document is part of the test set at least once. Setups We experiment with the following train-ing/test setups: In-domain : we train and test the models on the same dataset. Out-domain : we train the models on a single dataset and test them on the other two individually (e.g. train on Europolis and test on CMV and RegRoom). We also concatenate two datasets and test the corresponding model on the missing one, e.g. train on a joined set of CMV and RegRoom and test on Europolis ( 2vs1 ). with the following classification models (cf. section B.1 for more details and hyper-parameters):  Feature-based : we train a random-forest classifier with the features mentioned above (51 in total, appendix section 3.1). BoW : each contribution is represented as a count vector with the frequency counts for the 5000 most frequent words (the vocabulary was constructed based on the fusion of all datasets) which is fed into a random-forest classifier. FeedforwardNN : a contribution is represented as the average of the embeddings the words occuring in it and fed into a feed-forward neural network with one hidden layer of size 300 and a ReLU . BERT : we fine-tune BERT (Devlin et al., 2019) with a classification head on the task of predicting whether a contribution contains a report or not. Domain-adapted BERT (3 models) : we fine-tune the underlying language model (LM) with the masked language modeling objective and next sentence prediction on domains that would match the domains of our target datasets. For the Europolis dataset we sample  1M sentences from Europarl (Koehn, 2005) 6 , for CMV and RegRoom we sample  1M sentences from the Webis-CMV-20 corpus (Al-Khatib et al., 2020) and  1M sentences 6 We used the EN monolingual and the English translation from the DE-EN and FR-EN parallel data. from the args.me corpus (Ajjour et al., 2019). We fine-tuned one LM on each domain ( BERT-adapt-europarl , BERT-adapt-argue ) and one on the concatenation of the two ( BERT-adapt-mixed ). Results Table 11 in the Appendix displays the results for all models for each training setup (av-eraged over all splits), with significance values. The results show that BERT outperforms the other models for all training/test setups and training on the joined dataset ( all ) works well for all test corpora (the feature-based classifiers benefit especially from it). Fine-tuning the LM on the concatenated domains ( BERT-adapt-mixed ) yields the best results when trained on all with a macro F1-score of 0.76 (Europolis), 0.85 (CMV) and 0.94 (RegRoom). The non-domain-adapted BERT is more robust if in-domain and out-domain experiments are also taken into account, e.g. a drop in performance from 0.72 to 0.65 F1 can be observed on Europolis when trained in-domain using Bert-adapt-europarl . In the following section, we will employ linear regression to build a comprehensive picture of these performance trends. To get a statistically informed understanding of the different factors influencing the behavior of our models on the different datasets, we employ linear regression. Our dependent variables are aggregated performance (F1 macro) in section 5.1 and model predictions on individual items (proba-bility of report) in section 5.2. For the aggregated performance analysis in Section 5.1, our independent variables (IV) are: the different experimental configurations i.e., combinations of classifier architectures (referred to as 5533 "model" in the tables), training setup and test corpus as well as their interactions, as specified in the formula: 7 F1macro  (model + training setup + test corpus)3 For the model predictions (section 5.2) our independent variables are: the contribution-level features in 3.1 and a subset of the experimental configurations (training setup and test corpus), as well as the interactions between the contribution-level features and the experimental configurations. In both cases, our analysis proceeds in three steps. First, we fit incrementally complex models and assess their fit in terms of adjusted R 2 (propor-tion of explained variance) and significance with respect to the less complex models (i.e., models with fewer independent variables). At this step, we also check for multicollinearities. Next, once we identify the most explanatory regression model (i.e., the set of independent variables that maximizes the fit to the dependent variables), we proceed to identify its most explanatory IVs in terms of explained variance and significance (e.g., does the choice of training setup determine a strong difference in the performance of our classifiers?). Last, we identify the best values for the IVs (e.g., which of the training setups guarantees best F1?) by visualizing predicted performance with the help of effect displays (Fox, 2003), which show the partial effect of one (or more) parameters by marginalizing over all other parameters. With the regression analysis presented in this section, we are interested in capturing the pattern of variation exhibited by our experimental configurations, with a focus on the effects of in-domain, out-domain and cross-domain training and domain adaptation. Data : we consider all experimental runs from the in-domain, 2vs1 out-domain, and cross-domain training setups, resulting in 630 data points. 8 We code the levels of the IV training setup as in-domain (trained/tested on the same corpus), 2vs1 (out-domain training) and all (cross-domain train-ing). 7 The formula follows the R syntax, 3 denotes the 3-way interactions among the terms included between parenthesis, as well as their lower-order terms. 8 This number of data points results from the multiplication of: the number of classifiers (7), test corpora (3), training setups (3), splits (10). We conducted a sanity-check analysis with split id as IV, finding no significant effect. Results : Table 2 reports the fit of the simplest model, which only contains test corpus as IV, and of the incrementally more complex models. All IVs and interactions between explain a significant additional amount of variance 9 . The most explanatory model contains the three IVs and their two-way and three-way interactions, for a total explained variance of R 2 = 77 . 2% . Table 12 in the appendix displays the portion of variance accounted for by each IV in the final regression model. Most variance in performance (20% of the R 2 ) is determined by the classifier architecture (IV: model ).The high amount of additional variance explained by the three-way interaction, however, ( 12% ) indicates that the effects of the training setup and test corpus differ significantly between classifier architectures. What is the effect of training setup and domain-adaption? To get a more detailed picture of the relationship between experimental configurations, classifier architectures, and the predicted performance we can have a look at figure 1 which displays the effect plot for corresponding the three-way interaction. Comparing the different training setups, it becomes evident that cross-domain training setup is most robust: the difference between the predicted performance for all test corpora when trained on mixed domains ( training setup = all ), i.e. the difference between the colored lines at the rightmost position of the y-axis, is similar for the different classifier architectures (across all panels), while in-domain or out-domain training can lead to very different performance predictions. One can for example observe a low predicted performance when using features or BoW as classifier architecture for RegRoom in an out-domain setting (drop in the green line in the two upper panels in figure 1a). Out-domain training is rarely beneficial if no 9 p  0 . domain adaptation is performed, small positive effects can only be obtained for CMV and Europolis with the feature-based classifier (increase in the pink and blue line in the upper left panel in figure 1a). The largest positive effects for cross-domain training in contrast to in-domain training can be observed for Europolis (increase in the blue line from in-domain to all across most panels). Figure 1b compares the performance of the non-adapted BERT model (top-left panel) to its three domain-adapted variants. This comparison helps us further characterize the domain-specificity of the annotations. First of all, we can observe that BERT is more robust across different training setups and test-corpora. If we look at the course of the lines in the upper left panel in figure 1b we can see that it is relatively stable, almost parallel: the panel shows only a small drop in predicted performance for out-domain training and a small improvement with cross-domain training for RegRoom and Europolis. In contrast, the domain-adapted variants are subject to a much higher variance (e.g., a strong drop in predicted performance for BERTargue and BERTmixed, trained out-domain, tested on RegRoom). Domain-adaptation can be useful in a cross-domain training setup, as we can see slight improvements for the predicted performance comparing the colored lines at the right-most position of the y-axis of the upper left panel with the others, and this observation is similar for all domain-adapted variants. When comparing the different domain-adapted variants, the best model is BERTeuroparl which works well for all test corpora for out-domain and cross-domain training, whereas BERTargue leads to a low predicted performance when used with Europolis or in an out-domain training setup. This indicates that the LM adapted to a deliberative context (since Europarl contains parliamentary debates) is compatible with all test corpora, whereas BERTargue may be too domain-specific. In this section, we employ regression for error analysis, with the goal of finding out which linguistic properties of a contribution drive the model prediction away or towards the gold label. In this analysis, we focus on the predictions of the most robust classifier if both in-domain and out/cross-domain are taken into account, namely the non-adapted BERT (see section 5.1). We extract two subsets predictions from the BERT and perform regression analysis on each of them. We examine the predictions for the false positives (FPs) in order to find out in which cases the model overgeneralizes (which properties cause the model to predict a high probability for reports?), but also to find out what puts the model on the right track (probability close to the decision boundary). Similarly, we can examine the predictions for FNs, 5535 where typical features for reports may ensure that the model's probabilities go in the right direction, while particularly atypical features or a disadvantageous training/test combination may cause the model to incorrectly predict lower probabilities. Dependent variable In this analysis, our dependent variable is the probability that a comment contains a report . The distribution of the probabilities, however, is heavily skewed towards the upper and lower bound. We therefore transform the individual probabilities with a log transformation to reduce the skewness. In order to gain the same advantage for both error types and to be able to better compare the two in the effect plots, we invert the probabilities of the FPs and map them to the same range as the false negatives ( 1  p ( reports ) ). For both error types, the resulting values range between -10 and -0.5 and in both cases the predicted classification label would change to the gold label when the probability exceeds the upper threshold. 10 The distribution of the dependent variables is displayed in the histograms in section D.1. Thanks to this transformation, a positive effect or an increase in the dependent variable can be interpreted as beneficial for both error types. Independent variables and model selection In our analysis, we focus on the difference between the two largest datasets and include Europolis and RegRoom as test corpus , excluding CMV . Qualitatively, Europolis and RegRoom share the deliberative focus, while CMV is persuasion-driven and more likely to exhibit idiosyncratic properties that we would not be able to sufficiently discuss here for reasons of space. The training setup variable contains RegRoom , Europolis , and all so that we can examine the effects of in-domain, out-domain (this time in a 1vs1 version), and a cross-domain training setup. The final subsets for this analysis contain 776 datapoints for the FPs and 1,212 data points for the FNs. Our analysis builds on the assumption that specific linguistic properties of the input drive the predictions towards or away from the gold label. For this reason, the first core of IVs is represented by the contribution-level features used to train the feature-based random forest classifier (cf. section 3.1). To avoid multicollinearities and for simpli-10 We trained the regression models without a log transformation. This led to significantly worse results for the FNs, no large difference for the FPs. We therefore report the models with the log transformation. fication purposes we applied a correlation-based feature reduction methodology whose criteria are described in section D.2 in the Appendix. All features were further centered and scaled. We incrementally added the experimental configuration features ( training setup and test corpus ), as well as twoand three-way interactions, on top of the contribution-level ones. Similar to section 5.1 we compare nested models starting from the one containing only contribution-level features as IVs, effectively assessing whether the variation in performance is only due to linguistic properties or whether certain linguistic properties affect specific experimental configurations. Results : The results for both subsets (FPs and FNs) are shown in table 3. It is noticeable that the most explanatory regression model for the FNs can explain significantly more variance (40%) than the one for the FPs (10%); reasons for this could be the smaller amount of data and the poorer distribution of the dependent variable. In both cases, the contribution-level features alone explain roughly half of the variance accounted for by the most complex models. For the FPs, training setup and test corpus significantly contribute to the fit only when the two-way interactions are taken into account. On the other hand, for the FNs, all incremental steps significantly improve the fit. 11 Which feature types have the greatest impact on the errors? Table 4 summarizes the relative contribution of different feature groups to the total amount of explained variance. 12 The surface 11 To identify the most explanatory regression models (set of IVs) for FPs and FNs we employed step-wise model selection. A detailed discussion of this process, along with the list of selected IVs and their explained variance and significance can be found in appendix section D.2, in tables 13 and 14. 12 The sum of the explained variance ( R 2 ) of a feature group is the sum of the R 2 of the individual features in that group plus their interactions with training setup and/or test corpus .) The feature type "experimental configurations" contains only the amount explained by training setup , 5536 group of IV false positives false negatives experimental configurations 23 % 27 % surface features 0 % 3 % syntactic features 27 % 32 % sentiment/polarity 23 % 21 % textual complexity 27 % 17 % Table 4: Effect sizes (relative amount of R 2 ) for different groups of IVs in the most explanatory regression models for the FPs and FNs. based features have an extremely low impact on the prediction of the probability of reports: this is surprising given that previous work has shown that the length of a contribution has a great impact on model predictions ( length bias , cf. Wachsmuth and Werner (2020)). The other feature groups are all involved relatively equally in explaining performance variance, with textual complexity and syntactic features dominating FPs, and syntactic features FNs. What is the impact of contribution-level features? If we look at the effect plots for the most explanatory IVs, we can see which features are particularly helpful for the errors: positive effects drive the model towards the gold label. The effects for FNs highlight the dominant role of syntactic features. For example, we see positive effects for past tense verbs, personal pronouns, and post length (cf. figure 5, section D.2 in the appendix) which also confirm the hypotheses that these features are prototypical for reports. Some features are more discriminative when training on specific data: for example, the effect for personal pronouns is positive when training on RegRoom but slightly negative when training on Europolis (cf. figure 6, section D.2 in the appendix). While some of the features can lead to overgeneralization of the model we can identify a feature of textual complexity that proves to be useful for FPs. The effect plot in figure 7 (section D.2 in the appendix) displays the interaction between mean average type token ratio ( mattr50 ) and training setup . This feature puts the model on the right track when trained on RegRoom but moves the probability further away from the decision boundary when trained on Europolis or a mix. This example once again emphasizes that RegRoom, in combination with specific features, can take on an advantageous role as a training corpus. Storytelling vs. testimony: discussion The reports of personal experiences in RegRoom resemble prototypical narratives; they contain very personal and individual experiences, exhibiting many of the expected characteristics of a typical report. Our regression-informed error analysis shows that training on RegRoom positively impacts performance, while the opposite is often true for Europolis. Indeed, the reports in Europolis can describe more general experiences or a concrete situation in a country (e.g. in the example from Europolis, table 1). As a result, they are less prototypical and difficult to detect based on structural and linguistic features. Ideally, through training on Europolis or on mix, it is possible to recognize reports that are not only about an individual experience, but about a collective one, an aspect that is especially important in a discussion with a deliberative focus. An initial empirical investigation of the argument quality of the different types of reports gives evidence that reports exhibit a higher quality than contributions without reports (see section E in the appendix a detailed description of a pilot case-study). An interesting research question in this context is to what extent the individuality of a report influences quality or to what the commonality of the reported experience positively impacts deliberation. This work targeted the automatic identification of personal experiences and stories in argumentation. Leveraging available annotation in three argumentative datasets (two, Regulation Room and Change My View, from the Argument Mining community; one, Europolis, from the Deliberative Theory com-munity), we evaluated different classifier architectures and training setups. Mixing training data from different domains leads to robust results across all corpora and models and boosts the performance of the classifiers based on the domain-adapted LMs. 13 Our experiments established an empirical foundation that will allow us to investigate the target phenomenon at a larger scale and will lead to a better understanding of the compatibility of the underlying annotations (storytelling from Deliberative Theory and testimony in Argument Mining) as well as of the impact of storytelling/testimony on the quality of an argument. The research reported in this paper has been funded by Bundesministerium fr Bildung und Forschung (BMBF) through the project E-DELIB (Powering up e-deliberation: towards AI-supported modera-tion). We thank the anonymous reviewers for their valuable feedback. We would also like to thank Eva Maria Vecchi, Enrica Troiano, Sebastian Pad, and Jonas Kuhn for their feedback on different versions of this work. We also thank Marlne Gerber for giving us access to the Europolis dataset. Two of the three datasets employed in this work, Regulation Room and Change My View , are publicly available along with the annotation used in our experiments. Europolis has been kindly provided to us and the splits will be relased at the terms and conditions of its owners. Our experiments and analysis do not employ any author-specific feature. We cannot exclude, however, that the reporting of very specific personal experiences may facilitate the identification of the author of a post. As far as the societal impact of the use of reports of personal experiences and stories is concerned, we are fully aware that they can be a two-edged sword. On the one hand, as pointed out in the paper, the use of personal experiences triggers empathy and promotes inclusivity. On the other hand, however, the non verifiable nature of such reports makes them an easy vehicle to generate and spread fake news, and this, coupled with the emotional loading reports tend to have, makes them a useful tool highlight certain episodes for propagandistic purposes. Robust computational models to identify human-generated reports of personal experiences and a deeper understanding of the linguistic features of these contributions can also serve as a first step into the identification of false stories generated by automatic systems and of those human-generated ones that are best candidates to serve manipulation purposes.
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