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Appendix A: Dataset-related Specifications
A.1 Crawling Specifications
- M&A-specific terms used for crawling: one of merge, acquisition, agreement, acquire, takeover, buyout, integration + mention of a given company/acronym.
- Crawl start and end dates:
CVS_AET	15/02/2017 → 17/12/2018
CI_ESRX	27/05/2017 → 17/09/2018
ANTM_CI	01/04/2014 → 28/04/2017
AET_HUM	01/09/2014 → 23/01/2017
DIS_FOXA	09/07/2017 → 18/04/2018
A.2 Description and Examples of the Considered Labels
This is an extract from the annotation guidelines sent to the annotators.
The annotation process consists of choosing one of four possible labels, given a tweet and an M&A operation. The four labels to choose from are Support, Comment, Refute, and Unrelated.
Label 1: Support – If the tweet is supporting the theory that the merger is happening. Supporting tweets can be, for example, one of the following:
1. Explicitly stating that the deal is happening:  $\rightarrow$  [CI_ESRX] Cigna to acquire Express Scripts for $52B in health care shakeup via usatoday
2. Stating that the deal is likely to happen:
$\rightarrow$  [CVS_AET] CVS near deal to buy Aetna (Via Boston Herald)  $<  \text{URL}>$
3. Stating that the deal has been cleared:
$\rightarrow$  [CVS_AET] #Breaking DOJ clears #CVS $69 Billion deal for #Aetna.
Label 2: Comment - If the tweet is commenting on the merger. The tweet should neither directly state that the deal is happening, nor refute this. Tweets that state the merger as a fact and then talk about, e.g. implications or consequences of the merger, should also be labelled as commenting. Commenting tweets can be, for example, one of the following:
1. Talking about implications of the deal:
$\rightarrow$  [CI_ESRX] Cigna-Express Scripts deal unlikely to benefit consumers
2. Stating merger as fact and commenting on something related to the deal:
$\rightarrow$  [CVS_AET] #biotechnology Looking at the CVSAetna Deal One Academic Sees Major Disruptive Potential
3. Talking about changes in one or both of the companies involved:
$\rightarrow$  [CVS_AET] Great article about the impact of Epic within the CVS and Aetna Merge  $<URL>$
Label 3: Refute – This label should be chosen if the tweet is refuting that the merger is happening. Any tweet that voices doubts or mentions potential roadblocks should be labelled as refuting. Refuting tweets can be, for example, one of the following:
1. Explicitly voicing doubts about the merger:
$\rightarrow$  [ANTM_CI] business: JUST IN: Cigna terminates merger agreement with Anthem
2. Questioning that the companies want to move forward:
$\rightarrow$  [CI_ESRX] Why would  $\$ESRX$  want a deal with  $\$CI$ ?
3. Talking about potential roadblocks for the merger:
$\rightarrow$  [CI_ESRX] Why DOJ must block the CignaExpress Scripts merger  $\langle URL\rangle$
Label 4: Unrelated - If the tweet is unrelated to the given merger. Unrelated tweets can be, for example, one of the following:
1. Talking about something unrelated to the companies involved in the merger:
$\rightarrow$  [DIS_FOXA] I'm watching the Disney version of Robin Hood someone tell me how I have a crush on a cartoon fox
2. Talking about the companies involved in the merger, however not about the merger:  $\rightarrow$  [CVS_AET] CVS and Aetna's combined revenue in 2016 was larger than every U.S. company's other than Wal Mart <URL>
3. Talking about a different merger:  $\rightarrow$  [CVS_AET] What are the odds and which one do you think it will be? Cigna or Humana? Aetna acquisition rumor
Appendix B: Models-related Parameters
B.1 Encoder's Architectures
- SVMs: linear-kernel SVM leveraging bag of  $n$ -grams (over words and characters) features. A similar simple system outperformed all 19 teams in the SemEval-Task 6 (Mohammad et al., 2017).
- MLP: a multi-layer perceptron (MLP) with one dense layer, taking as input the concatenation of tweet's and target's TF-IDF representations and their cosine similarity score (similar to the model in Riedel et al. (2017)).
- EmbAvg: a MLP with two dense layers, taking as input the average of the tweet's and the target's word embeddings. Averaging embeddings was proven to work well for Twitter data in previous papers by Zubiaga et al. (2016); Kochkina et al. (2017), who - differently than in this paper - classified stream of tweets in a conversation tree.
- CharCNN and WordCNN: two CNN models, one over character and one over words, following the work by Vijayaraghavan et al. (2016).
- BiCE: a similar Bidirectional Conditional Encoding model to that of Augenstein et al. (2016): the tweet is processed by a BiLSTM whose forward and backward initial states are initialized with the last states of a further BiLSTM which processed the target.
- CrossNet: a BiCE model augmented with self-attention and two dense layers, as in the cross-target stance detection model (Xu et al., 2018).
- SiamNet: siamese networks have been recently used for fake news stance detection (Santosh et al., 2019). Here we implement a siamese network based on a BiLSTM followed by a self-attention layer (Yang et al., 2016). The obtained tweet and target vector representations are concatenated with their similarity score (following Mueller and Thyagarajan (2016), we used the inverse exponential of the Manhattan distance).
- Co-MatchAtt: we use a similar co-matching attention mechanism as in Wang et al. (2018) to
connect the tweet and the target, encoded with two separated BiLSTM layers, followed by a self-attention layer (Yang et al., 2016).
- TAN: a model combining a BiLSTM and a target-specific attention extractor over target-augmented embeddings (Du et al., 2017; Dey et al., 2018), similarly as in Du et al. (2017).
- HAN: we follow Sun et al. (2018) to implement a Hierarchical Attention Network, which uses two levels of attention to leverage the tweet representation along with linguistic information (sentiment, dependency and argument).
SVM model
Word NGrams	1,2,3
Char NGrams	2,3,4
Common to all neural models
max tweet len	25
batch size	32
max epochs	70
optimizer	Adam
Adam learning rate	0.001
word embedding size	200
embedding dropout	0.2
TFIDF-MLP model
BOW vocabulary size	3000
dense hidden layer size	100
EmbAvg model
dense hidden layers size	128
WordCNN model
window size	2,3,4
no filters	200
dropout	0.5
CharCNN model
no of stacked layers	5
window size	7,7,3,3,3
no filters	256
dropout	0.2
BiCE, CrossNet, SiamNet and TAN model
BiLSTM hidden size	265*2
BiLSTM recurrent dropout	0.2
HAN model
max sentiment input len	10
max dependency input len	30
max argument input len	25
BiLSTM hidden size	128
Table 6: Hyperparameters used for training. Whenever reported, we used the same as in the original papers.
B.2 Preprocessing Details
After some preliminary experiments, we found the following preprocessing steps to perform the best:
1. Lowercasing and tokenizing using NLTK's Tokenizer8.
2. Digits and URL normalization.
<sup>8</sup>https://www.nltk.org/api/nltk(tokenize.html
3. Low-frequency users have been normalized; high frequency users have been kept, stripping the "@" from the token. Such users included the official Twitter accounts of the companies involved in the mergers (like @askanthem), media (@wsj), official accounts of US politicians (@potus, @thejusticedept,...)
4. The # signs have been removed from hashtags.
We keep in the vocabulary only tokens occurring at least 3 times, resulting in 19,561 entries considering both healthcare and entertainment industry.
We use gensim to extract the TF-IDF vectors from the data<sup>9</sup>, which are used in the TFIDF-MLP model. For the HAN model, following Sun et al. (2018), we use the MPQA subjective lexicon (Wilson et al., 2005) to extract the sentiment word sequences and the Stanford Parser<sup>10</sup> to extract the dependency sequences. We train an SVM model to predict argument labels on Hasan and Ng (2013)'s training data, and we predict the argument sentences for the WT-WT dataset, as discussed in Sun et al. (2018).
$^{9}$ https://radimrehurek.com/gensim/models/tfidfmodel.html
$^{10}$ https://nlp.stanford.edu/software/lex-parser.html