Research in stance detection has so far focused on models which leverage purely textual input. In this paper, we investigate the integration of textual and financial signals for stance detection in the financial domain. Specifically, we propose a robust multi-task neural architecture that combines textual input with high-frequency intra-day time series from stock market prices. Moreover, we extend WT  WT , an existing stance detection dataset which collects tweets discussing Mergers and Acquisitions operations, with the relevant financial signal. Importantly, the obtained dataset aligns with STANDER , an existing news stance detection dataset, thus resulting in a unique multimodal, multi-genre stance detection resource. We show experimentally and through detailed result analysis that our stance detection system benefits from financial information, and achieves state-of-the-art results on the WT  WT dataset: this demonstrates that the combination of multiple input signals is effective for cross-target stance detection, and opens interesting research directions for future work. Stance detection (SD) is the task of automatically classifying the writer's opinion expressed in a text towards a particular target (Kk and Can, 2020). Starting from Mohammad et al. (2016)'s seminal work, research on Twitter SD gained increasing popularity (Ghosh et al., 2019), embracing new topics (Derczynski et al., 2017; Aker et al., 2017a; Conforti et al., 2020b) and languages (Gorrell et al., 2019; Vamvas and Sennrich, 2020a; Zotova et al., 2020). In recent years, research on SD has mainly focused on cross-target generalization, in which an SD system is tested on targets unseen during training (Xu et al., 2018). Cross-target generalization constitutes one of the biggest challenges in Twitter SD (AlDayel and Magdy, 2021): in this context, researchers investigated a wide range of techniques, including adversarial training (Wang et al., 2020; Allaway et al.), cross-lingual transfer (Mohtarami et al., 2019), knowledge transfer using semantic and emotion lexicons (Zhang et al., 2020), weak supervision through synthetic samples (Conforti et al., 2021b; Li and Caragea, 2021), and various types of cross-domain transfer (Schiller et al., 2021; Hardalov et al., 2021a). In this paper, we study multimodality as a means to enhance cross-target generalization in Twitter SD. Multimodal Machine Learning studies the integration and modeling of multiple modalities (Elliott et al., 2016), where a modality refers to the way in which something happens (Baltrusaitis et al., 2019). Our contributions are as follows: 1. We study multimodal learning for Twitter SD. Despite being an established research area in NLP (Elliott et al., 2016), SD in a multimodal context is still understudied. 2. We extend WT  WT , an SD dataset which collects English tweets discussing four Mergers and Acquisitions operations (M&As or mergers , Conforti et al. (2020b)), with high frequency intra-day stock market data for the involved companies, which we release for future research 1 . We note that the union of our financial signal with WT  WT and with STANDER , an SD corpus collecting news articles discussing the same mergers (Conforti et al., 2020a), will constitute the first multi-genre, multi-modal parallel resource for SD and, more generally, one of the very few of this kind in NLP. 3. We propose SDTF ( S tance D etection with T exual and F inancial signals), a novel multitask, multimodal architecture for Twitter SD, which integrates textual and financial signals. 1 https://github.com/cambridge-wtwt/ acl2022-wtwt-stocks 4074 4. Finally, we show experimentally that SDTF benefits from the information encoded in the financial signal, achieving state-of-the-art results on the WT  WT dataset; the integration of multiple input signals thus constitutes a promising research direction to tackle cross-target generalization for SD. We study SD in the financial domain and consider tweets discussing M&A operations, i.e. financial transactions in which the ownership of a company (the target ) is transferred to another company (the buyer , Bruner and Perella (2004)). An M&A process usually comprises many stages, ranging from informal talks between the companies' boards to acquisition planning, negotiations, and external approvals, up to the closing of the deal (or its rejection, e.g. by antitrust bodies). M&As account for billions of dollars of investment globally and have been widely studied under many aspects (Gomes and Maldonado, 2020). They are well known in NLP (Lefever and Hoste, 2016; Yang et al., 2020; Conforti et al., 2020a,b) and constitute an important application in other AI fields, with a strong focus on automatic prediction of the M&A outcome (Yan et al., 2016; Jetley and Ji, 2010; Moriarty et al., 2019; Venuti, 2021). In our task, a model receives a tweet and a target merger, and has to predict the stance expressed by the tweet's author with respect to the likelihood of the merger to succeed:  Target. Stance. Refute All existing models for financial SD only leverage the tweet's text as input (Conforti et al., 2020b; Liang et al., 2021; Li and Caragea, 2021). However, a user tweeting at a particular time is immersed into a context which shapes their view of the world: their opinion about an M&A's outcome will be influenced by how the involved companies are perceived. In this paper, we use a variation of the stock market prices from the n days prior to a tweet's posting as a means to provide a model with such context. According to the Efficient Market Hypothesis (Fama, 1970), stock market prices reflect all publicly known information. Even though the Efficient Market Hypothesis is controversial (Malkiel, 2003), stock market prices still reflect a considerable amount of publicly known information. Therefore, we argue that they can be used as a proxy for the available knowledge about the merger at a given time. The relationship between rumors about an M&A operation and their effect on the involved companies' stocks is mutual and has been widely studied in finance (Ma and Zhang, 2016; Betton et al., 2018; Jia et al., 2020; Gorman et al., 2021; Davis et al., 2021), but never investigated in NLP. To our knowledge, the integration of textual and financial data signals has been studied for financial forecasting (Schumaker and Chen, 2009; Hu et al., 2018; Sawhney et al., 2020a,b, 2021c; Ni et al., 2021), but has yet to be investigated for SD. Traditionally, research on SD has focused on user-generated data, such as blogs and commenting sections on websites (Skeppstedt et al., 2017; Hercig et al., 2017), apps (Vamvas and Sennrich, 2020b), online debate forums (Somasundaran and Wiebe, 2009), Facebook posts (Klenner et al., 2017) and, above all, Twitter. Since Mohammad et al. (2016)'s seminal work, Twitter has been used as a data source for collecting corpora covering a wide range of domains, from US politics (Mohammad et al., 2017; Inkpen et al., 2017) to mental health (Aker et al., 2017b), breaking news events (Zubiaga et al., 2016; Gorrell et al., 2019), finance (Conforti et al., 2020b), and the COVID pandemic (Hossain et al., 2020; Glandt et al., 2021). SD has been studied both as a stand-alone, isolated task, and integrated as a sub-component of more complex NLP pipelines (Hardalov et al., 2021b). Starting from the pioneering work by Vlachos and Riedel (2014), SD has been identified as a key step in fake news detection (Lillie and Mid-delboe, 2019) and automated fact-checking (Popat et al., 2017; Thorne and Vlachos, 2018; Baly et al., 2018). Multimodal learning has proven successful for many NLP tasks (Tsai et al., 2019; Zadeh et al., 2020), including grounding (Beinborn et al., 2018), visual question answering (Ben-Younes et al., 2017; Yu et al., 2018), sentiment analysis (Rahman et al., 2020), and humor detection (Hasan et al., 2019). dataset exists for multimodal SD, MULTISTANCECAT (Taul et al., 2018; Segura-Bedmar, 2018), released for IberEval2018 2 . MULTISTANCECAT collects 11,398 tweets in Spanish and Catalan discussing the Catalan 2017 Independence referendum: according to Taul et al. (2018), the corpus is multimodal because it contains, along with the tweets' text, contextual information and up to 10 images downloaded from the authors' timeline. We note that, unfortuntately, almost all research building on MULTISTANCECAT considered only the provided textual features, thus ignoring its multimodal component. As mentioned in Taul et al. (2018, p. 157), only 1 out of the 4 teams participating in the task integrated images into their model, by training a CNN on Spanish and Catalan flags (with the underlying intuition that using them would hint to the user's stance with respect to the topic of Catalan independence) 3 . Interestingly, no positive impact was observed on SD results when including such multimodal signals. Our work differs in a number of respects: (1) the size of our corpus is considerably larger, thus allowing for more robust training; (2) we do not consider visual signals, such as images, but  consistently with WT  WT 's domain  financial time-series signals from stock market prices; and (3) most notably, MULTISTANCECAT 's multimodal signal consists 2 http://www.autoritas.net/ MultiStanceCat-IberEval2018/ 3 The team did not submit working notes describing their system; therefore, we refer to the model's overview provided in the general task paper (Taul et al., 2018). of a maximum of 10 images taken from the user's timeline: therefore, the images might not be related to the tweet, might have been posted at a very different timestamp, or might be the same for multiple tweets published by the same author. In contrast, our financial signal is specific to each tweet and is perfectly aligned with its time of posting. In recent years, there has been an increasing interest in research at the intersection between finance and NLP (Hahn et al., 2018; El-Haj et al., 2018), with a rich stream of work focusing on financial textual analysis (Lang and Stice-Lawrence, 2015; Loughran and McDonald, 2016), sentiment analysis (Giachanou and Crestani, 2016; Chan and Chong, 2017; Krishnamoorthy, 2018), stance detection (Conforti et al., 2020b,a, 2021a), volatility prediction (Rekabsaz et al., 2017; Kolchyna et al., 2015) and, above all, financial forecasting (Qasem et al., 2015; Ranco et al., 2015; Pagolu et al., 2016; Pimprikar et al., 2017; Oliveira et al., 2017). While multimodality has not been investigated for financial SD, it constitutes a very active research direction in financial forecasting, i.e. the task of predicting a business' future financial performance (Abu-Mostafa and Atiya, 1996). Given the importance of psychological and behaviorial elements on stock-price movements (Malkiel, 2003), researchers in economics 4076 have started to explore models which leverage features beyond simple numerical values (Nikou et al., 2019; Liu and Chen, 2019). In this context, a stream of work analyzed the integration of historical price data with social media texts (Sawh-ney et al., 2020a) and other audio or textual features (Zhao et al., 2019; Qin and Yang, 2019; Sawhney et al., 2021b; Lee and Yoo, 2020; Sawhney et al., 2021b,a; Das et al., 2021; Chen and Huang, 2021). Text Signal. As our text signal, we use Will-They-Won't-They (Conforti et al., 2020b, WT  WT ) 4 , which collects English tweets discussing four M&As between US companies (Table 1). WT  WT is expert-annotated for stance with respect to the likelihood of the merger happening according to the opinion expressed in the text, following a four-class classification schema: support, refute, comment and unrelated (i.e. the tweet does not discuss the merger). Below, we report one example for each of the considered labels (targets in squared brackets):  Support [ CVS _ AET ] CVS, Aetna $69B merger wins DOJ approval < URL >  Refute [ ANTM _ CI ] Big-name lawmakers want to block Aetna-Humana and Anthem-Cigna! Comment [ ANTM _ CI ] Anthem-Cigna deal would create Big 3': If the deal is approved  Unrelated [ CVS _ AET ] Urge Your Legislators to Oppose CVS and Walmart Takeover of Medical Care Delivery!!! < URL >#MSSNY Financial Signal. For the four healthcare M&As in WT  WT 5 , we obtain historical prices in 30-min intervals for the involved stocks. The financial data has been bought from FirstRate Data LLC 6 (  700MB) at market price. Each entry in the data has the following fields: DateTime, Open, High, Low, Close, Volume . DateTime is in US Eastern Time, in the format YY-MM-DD h:m:s . Only minutes with trading volume are included: times with zero volume, such as during weekends or holidays, are omitted. Prices 4 WT  WT can be downloaded, upon signing a data sharing agreement, from its GitHub repository https://github. com/cambridge-wtwt/acl2020-wtwt-tweets 5 Note that this aligns with the targets collected in STANDER , a news SD corpus (Conforti et al., 2020a). 6 https://firstratedata.com/ are adjusted for dividends and splits 7 . We used Python's datetime library to align Twitter time values (UTC) with the financial signal (EST, New York Stock Exchange) 8 Note that price variations in 30-minutes intervals are considerably more granular than the financial signal used in NLP work, which is mostly limited to daily data (Sawhney et al., 2020a). Such granularity is necessary when monitoring tweets, which are highly reactive to real-time, on-topic information from the outside world (ALRashdi and O'Keefe, 2019). Analysis. Figure 1 shows an example of the integration of the two signals. On the day the antitrust complaint was made to the Department of Justice regarding the M&A operation, ANTM 's price increased while CI 's decreased. Such movements testify that the event changed the world's view: people believe that the merger is less likely to happen, and this is reflected by their investment decisions. The direction of the price variation reflects standard M&A theory (Bruner and Perella, 2004): the buyer will not buy the target's shares at a premium, thus the owners of target's stocks will not profit from the acquisition. The price variation is useful for classifying a tweet on that day, as it implies that the likelihood of a refute label is higher. This is reflected in the tweet distribution in the lower part of the Figure: the distribution of tweets on that day shows that most of them were indeed refuting . We report one more example in Appendix A. 5 Models As shown in Figure 2, our multitask SDTF model is composed of a textual , a financial and a multimodal component. Following previous work in SD (Hardalov et al., 2021a), we obtain a vector representation h text  R d for the textual input by averaging the token-level hidden states from the last layer of a large transformer (in our case, BerTweet (Nguyen et al., 8 The timestamps of posting of each tweet in the WT  WT dataset can be shared in accordance with the terms of use outlined by Twitter https://developer.twitter.com/ en/developer-terms/agreement-and-policy . No private information (such as username of the tweet's author and similar) is shared. where target consists of the string: B ( b, t b will merge with T ( t, t t ) , where B , b , and t b , are the buyer's name, acronym and Twitter username 9 (same for the target company). Input. For each tweet posted at time s , we consider a window of w days in the past. At each timestep i , in { s  w, s  w + 1 , ..., s } , we consider two price vectors p bi , p ti  R 12 which consist of: (1) p bi = p bi 1  p bi 2  p bi 3 = [ o b , c b , h b , l b ]  [ o m , c m , h m , l m ]  [ v b , r b , c b c m r b c m ] where o , c , h , l and v are resp. the opening, closing, highest, lowest price and volume of transactions at time i for the buyer's stock (superscript b ) or for the overall market index (superscript m ); finally, r is the return at time i and is defined as ( c bi  c bi  1 ) /c bi  1 (Law (2018), same for the target). Price Embeddings . We obtain a vector representation e ib for each time point i by concatenating: p bi  e bi 1  e bi 2 (2) 9 For example, Anthem (ANTM, AnthemInc) . This is in principle the same as in (Liang et al., 2021), with two differences: we add the companies' official Twitter usernames and, similarly to other SD works (Hardalov et al., 2021a), we consider first the input text, and then the target. where e bi 1 and e bi 2 are the time embeddings for p bi 1 and p bi 2 (same for the target). We use Time2Vec (Kazemi et al., 2019) for time embeddings, and we jointly learn embeddings for the buyer and the target. Price Encoder . As in Du and Tanaka-Ishii (2020) and Kostkova et al. (2017), we use a Gated Recurrent Unit (Cho et al., 2014, GRU) to encode the price variations over time. We implement two separate GRU b and GRU t for the buyer and the target. At time i , the GRU b 's output consists of: h i = GRU b ( e ib , h i  1 ) s  w  i  s (3) To model the inter-dependencies between the two stocks, we use multi-head attention mechanism (Vaswani et al., 2017) which, in our experiments, proved to be more effective for SD than the classic temporal attention used in financial forecasting (Feng et al., 2019). In practice, we obtain a unified price vector representation h price as: h b =  b ( H t , H b ) (4) h t =  t ( H b , H t ) (5) h price = h b  h t (6) where  b and H b (resp.  t and H t ) are the buyer's (and target's) multi-head attention mechanism and the matrix consisting of GRU b 's (resp. GRU t 's) outputs. Signals from different modalities encode complementary information (Schumaker and Chen, 2009): we avoid simple concatenation (Li et al., 2016), which would treat such signals equally, and implement a bilinear transformation to integrate the tweet's encoded representation with the historical prices of the involved companies (Sawhney et al., 2020a). Given the price and the text vector representations h price  R p and h text  R d , we obtain a combined vector representation h  R w as: h = relu ( h Ttext W h price + b ) (7) where W  R w  d  p and b  R w are the learned weight matrix and bias. Stance Detection. We expect the financial signal to be relevant only in the case of related stance labels (i.e. support, refute, comment ). In order to assist the model in differentiating between those two macro-classes, we predict a binary label re-lated/unrelated along with the stance label y stance : y stance = softmax ( h ) y binary =  ( h text ) (8) Financial Forecasting. As it has been previously studied in finance, rumors about a merger can affect the stock prices of the involved companies (Jia et al., 2020; Davis et al., 2021). To encourage our model to learn such influence, we also add two binary financial-related outputs, in which we predict the stock movement of the two companies: y buyer =  ( h buyer ) (9) y target =  ( h target ) (10) where h buyer (resp. h target ) is the concatenation of the last output vector of GRU b and h , and y buyer (resp. y target )  { , } (i.e., stock closing price for the considered company will resp. move up, or fall). The final loss is: L = L stance + 0 . For L stance we use categorical cross-entropy loss, while L binary , L buyer and L target use binary cross-entropy loss function. The weights of the last three loss components were empirically set in an initial pilot. Preprocessing. We perform minimal preprocessing on the textual signal. Concerning the financial signal, we consider a window of 30 timepoints in the past, and price variations every 30 minutes: depending on the tweet's posting time, this accounts for the previous  2.5 days 10 . For FF, we predict ups or downs in the considered company's closing price 2 hours after the tweet 11 (see Appendix B.1 for details). Training Setup and Evaluation. Details on the training setup and (hyper-)parameter settings are reported in Appendix B.2 for replication. Following Hanselowski et al. (2018); Conforti et al. (2020b), we consider macro-averaged precision, recall and F 1 score. To account for performance fluctuations (Reimers and Gurevych, 2017), we average three runs for each model (standard deviation is reported in Appendix B.2). SVM , a linear-kernel SVM leveraging bag of ngrams (over words and characters) features, similar as in Mohammad et al. (2017);  CrossNet , a cross-target SD model (Xu et al., 2018) consisting of a bidirectional conditional encoding model over LSTMs, augmented with self-attention and two dense layers;  SiamNet , a siamese network similar to San-tosh et al. (2019), which is based on a BiL-STM followed by a self-attention layer;  HAN , a Hierarchical Attention Network as in (Sun et al., 2018)) which uses two levels of attention to leverage the tweet representation along with linguistic information (sentiment, dependency and argument); and two further baselines from Liang et al. (2021): BERT , a strong vanilla BERT-based model fine-tuned on WT  WT ;  TPDG , a sophisticated network based on a target-adaptive pragmatics dependency graph. 10 During night or holidays, price entries are usually not available. Tweets published outside of the market's opening hours (9:30am4pm EST during workdays) are thus associated with the most recent available financial signal. 11 Or, for tweets posted at night or during holidays, the first available closing price in the future. Table 3 shows our experimental results. We observe that using BerTweet as main text encoder alone achieves considerable gains in performance with respect to all stance labels considering all baselines, including the strong vanilla BERT baseline. This is unsurprising, given the peculiarities of Twitter language (Hu et al., 2013) which are captured by BerTweet. Adding the financial signal. Adding our financial component proves to be effective over all considered targets, with improvements in F 1 scores up to +5.8 ( AET _ HUM ). Single-label performance seems to suggest that price variations encode very useful information for all labels, resulting in notable improvements not only on the unrelated (+3.7), but also on the refute and support samples (resp. +2.1 and +5.4 in accu-racy): this is important because those labels, apart from being the minority classes, arguably constitute the most relevant information for downstream tasks (Scarton et al., 2020). Adding Multi-Task Objectives and Ablation Experiments. Results of ablation experiments (Ta-ble 3) show that including the financial forecast (+FF) task alone brings moderate improvements in performance, while considering binary SD (+Bi-nary) alone moderately degrades it: their combination, however, achieves the best results over three of the four mergers. Interestigly, jointly modeling FF and binary SD seems to be beneficial not only for SD: as shown in Table 4, best results on both ancillary tasks are obtained in the multitask setting. Binary SD performance is very satisfactory over all mergers, with a correlation with M&As with a higher proportion of unrelated samples. Moving to the other ancillary tasks, FF results are encouraging 12 , even if we considered a considerably shorter time window of historical pricing than architectures specifically designed for FF (Du-mas et al., 2009; Kim et al., 2019; Ho et al., 2021). This suggest that the learned multimodal textual and financial vectors constitute an informative input for the FF predictors. Single-Label Performance. An analysis of single-label performance (Table 3) shows that models including the financial component, with or without ancillary tasks, achieve best performance on all related labels. 12 Consider for example a strong neural model such as Selvin et al. (2017), reported in (Sawhney et al., 2020a). A similar situation, in which a model leveraging simple lexical features achieved best results on the unrelated samples, was already observed not only for WT  WT (Conforti et al., 2020b), but also for other SD datasets, such as FNC-1 (Pomerleau and Rao, 2017; Hanselowski et al., 2019). We note that, in both datasets, related-unrelated vs. support/comment/refute classifications can be seen as constituting two different tasks: the former is more similar to topic detection, where even surface-level methods can do well, whereas the latter is an inference task which requires deeper semantic knowledge (Conforti et al., 2018) 13 . The analysis of the confusion matrices (reported in detail in Appendix B.2) shows that most errors concern support or refute samples which were mis-classified as comment : as already observed in Conforti et al. (2020b), the difference between a comment and a stance-bearing label such as support (or refute ) depends on argumentative nuances in the tweet, which are sometimes subjective and ultimately depends on the annotator's preferences. A number of comment-unrelated misclassifications are also present, especially for M&As with a high number of unrelated samples (such as CVS _ AET and ANTM _ CI ). Performance When Silencing Different Signals. In order to estimate the relative importance of the two signals considered in the SDTF model, we consider a scenario in which we silence one of the two signals: for the textual signal, this cor-13 We note that, in a practical scenario, it might make sense to first apply a simple lexicon-based method for filtering out unrelated samples, and then to adopt a more sophisticated approach for the second step, as proposed for example by Masood and Aker (2018). responds to replacing the target and the tweet's text with two empty strings (i.e., [CLS] [SEP] [SEP] as input to the right component in Figure 2); for the financial signal, we input two empty price vectors for the considered companies (i.e. the left components in Figure 2). Results of such ablation experiments (Table 5) show that, as expected, the textual signal provides the biggest contribution for SD, and the financial signal alone is not sufficient at all to perform SD. Blending together both signals, however, provides the most informative input to the model: a consistent drop in performance over all labels, including unrelated , is observed with models exposed to empty price vectors. Robustness Over Parameters Freezing. Moreover, we investigate the model robustness over freezing BerTweet 14 : we consider two scenarios, in which we freeze the complete weights or BerTweet, or all but its last three layers (Wang et al. (2019), see Appendix B.2 for details on number of parameters for the different settings). As expected (Mosbach et al., 2020), performance degrades with fewer layers trained (Table 6), with the exception of the BerTweet architecture when freezing all but its last three layers. Notably, our multitask SDTF model is more robust over parameter freezing than the vanilla BerTweet, achieving higher performance over all considered metrics: this suggests that, when less powerful textual encoders are provided, the presence of the financial signal supports SD classification. Adding Synthetic Data. As mentioned in the Introduction, a recent stream of work investigates the usage of synthetically generated data to compensate for data scarcity in Twitter SD. In particular, Li and Caragea (2021) used Auxiliary Sentence based Data Augmentation (ASDA), a conditional 14 This is important, because the number of trainable parameters correlates with CO 2 emission (Strubell et al., 2019). data augmentation method, to double the size of SD datasets, achieving state-of-the-art results on WT  WT with a model trained on the union of gold and synthetic samples. In a last set of experiments, we investigate the impact of adding such synthetically generated examples to an SDTF model. As synthetic samples aren't associated to any price vectors from the stock market, we proceed as follows: we first fine tune a BerTweet model on ASDAWT  WT , which we obtain from the ASDA paper's authors; then, we use such model's weights to initialize the textual encoder of an SDTF multitask model (the left components in Figure 2), which we finally train on the gold WT  WT as described in Section 5. Results in Table 7 show that models trained on ASDAWT  WT (gold and synthetic samples) achieve better results than SDTF trained on gold data alone. Including synthetic signal from ASDAWT  WT seems to be effective for all considered training settings: even using a simple pretraining strategy as described above allows an SDTF model to capture useful textual features from the synthetic samples, which are retained over the finetuning stage and allow for better cross-target generalization. Our finetuned model (ASDA+SDTF in Table 7) reaches state-of-the-art results on the WT  WT dataset and best results over three of the four considered mergers, with gains in F 1 scores ranging from +1 . 4 ( ANTM _ CI ) to +3 . 2 ( CI _ ESRX ). In this paper, we studied the well-established task of Twitter SD in a multitask scenario, focusing on the financial domain. We proposed SDTF, a novel model which integrates two modalities, text and financial time series data. We extended WT  WT , a large dataset for financial SD, with financial signals from stock market prices. Our detailed analysis of models' results demonstrated that financial SD on tweets benefits from such signals: models which include textual and financial features showed better cross-target generalization capabilities, and obtained better results on all stance labels. Finally, we proposed a simple but effective setting to leverage useful signals encoded in synthetic samples, reaching state-of-the-art results on WT  WT . We release the financial signal collected to complement WT  WT : together with the STANDER corpus of news SD, which discusses the same mergers, it constitutes an invaluable and unique resource to foster research on multi-modal, multi-genre SD, and to model the integration and mutual influences between stock market variations, tweets, and authoritative news sources. Data Collection. Daily financial data is publicly available and can be freely downloaded (e.g. through Yahoo Finance 15 ). However, granular financial data needs to be purchased. We bought the historical financial data from FirstRate Data LLC 16 , who source their data directly from major exchanges. We tested all signals for consistency and completeness, and found that it reflects the actual trading in the stocks. Presence of Bias. As textual input, we used WT  WT , a publicly available dataset which we obtained from the authors after signing a data sharing agreement (Academic Free License). Given that many NLP tasks are somehow subjective (Poesio et al., 2019), and the choice of annotators might reinforce the emergency of bias (Waseem, 2016; Sap et al., 2019; Geva et al., 2019) we note that WT  WT might contain annotation bias, which could be amplified by our models (Shah et al., 2020; Waseem et al., 2021). Moreover, the BerTweet model we are using as main text encoder might encode biases due to the data it was trained on (Bender et al., 2021). We observe, however, that both elements are beyond our control. Data Sharing. In accordance with FirstRate Data, we release the relevant portion of the data under Academic Free License at the link: https: //github.com/cambridge-wtwt/ acl2022-wtwt-stocks . We are aware of the many ethical issues surrounding social media research (Hovy and Spruit, 2016). Virtually all models trained on social media data are dual-use (Benton et al., 2017): in order to avoid 15 https://uk.finance.yahoo.com/ 16 https://firstratedata.com/ 4082 potential misuse, we will share our financial signals, which is complementary to WT  WT , only upon signing a data sharing agreement restricting the data usage to research only. Environmental Factors. We are conscious that training transformers such as BerTweet produces large quantity of CO 2 emissions (Strubell et al., 2019; Henderson et al., 2020). We observe that, in our case, we are not training such models from scratch, thus considerably limiting the training time. Moreover, we also experimented with (partially) frozen transformers (Lee et al., 2019; Sajjad et al., 2020; Mosbach et al., 2020), which in turn require less parameters to be optimized. We thank the anonymous reviewers of this paper for their efforts and for the constructive comments and suggestions. We gratefully acknowledge funding from the Keynes Fund, University of Cambridge (grant no. JHOQ). CC is grateful to NERC DREAM CDT (grant no. 1945246) for partially funding this work. CG and FT are thankful to the Cambridge Endowment for Research in Finance (CERF).
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