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A Evaluation Metrics
A.1 MCC
Considering imbalance in our dataset we use MCC to evaluate all our models as it takes into account
true and false positives and negatives and is generally regarded as a balanced measure. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. Formally, given a confusion matrix  $\left( \begin{array}{ll}tp & fn\\ fp & tn \end{array} \right)$  MCC is defined as,
$$
M C C = \frac {(t p \times t n) - (f p \times f n)}{\sqrt {(t p + f p) (t p + f n) (t n + f p) (t n + f n)}} \tag {12}
$$
A.2 F1 score
F1 score is the harmonic mean between precision and recall. The F1 score is defined as,
$$
F 1 = \frac {2}{\text {r e c a l l} ^ {- 1} + \text {p r e c i s i o n} ^ {- 1}} \tag {13}
$$
$$
p r e c i s i o n = \frac {t p}{t p + f p} \tag {14}
$$
$$
r e c a l l = \frac {t p}{t p + f n} \tag {15}
$$
A.3 Exact Match
EM measures the percentage of overall predicted bubbles that exactly match the true bubble spans.
$$
\text {E x a c t} = \frac {t p + f p}{t p + t n + f p + f n} \tag {16}
$$
B Experimental settings
We implement MBHN using the Pytorch framework. The Hyperbolic module of MBHN is based on the implementation  $^{5}$ . Our MBHN has a total of 858 and 44,520 parameters with price and text as input respectively. We utilize the grid search to find all optimal hyperparameters based on the validation MCC scores for all models. As an optimiser we use Adam with  $\beta_{1} = 0.9$  and  $\beta_{2} = 0.999$  and an L2 weight decay of  $1e - 5$ . We use early stopping based on the Accuracy score over the validation set.
C Dataset Curation And Preprocessing
To create CryptoBubbles dataset, we select top 9 cryptocurrency exchanges and choose around 50 most traded cryptocoins by volume from each exchange and obtain 456 cryptos in total as shown in Table 7. For these cryptos we mine 5 years of daily price data consisting of opening, closing, highest and lowest prices from  $1^{\text{st}}$  Mar'16 to  $7^{\text{th}}$  Apr'21 using CryptoCompare. Next, we extract
<sup>5</sup>https://github.com/ferrine/hyrnn
$^{6}$ https://www.cryptocompare.com/
cryptocurrency related tweets under Twitter's official license. Following (Xu and Cohen, 2018a), we extract crypto-specific tweets by querying regex ticker symbols, for instance, "$DOGEB" for DogeCoin. We mine tweets for the same date range as the price data and obtain 2.4 million tweets. We observe that the number of tweets increases every year, suggesting the popularity of speculative trading using social media.
To identify bubbles, we use the PSY model (Phillips et al., 2015b) which is a widely used exuberance detection method in financial time-series analysis (Cheung et al., 2015; Harvey et al., 2016). Following (Corbet et al., 2018) we feed the closing prices of each cryptocurrency to the PSY model which outputs date spans for each bubble having labels 1 or 0 denoting whether the day is included in the bubble or not. We rank all of our cryptos by the trade volume and delete some of the lower ranked cryptos having no bubble. After this step we obtain with a set of 404 cryptos.
To generate a data sample belonging to a crypto  $c_{i}$ , we first pick  $T$  lookback dates and  $\tau$  lookahead dates. Now, for each lookback day we randomly choose 15 tweets and chronologically append the tweets. For the corresponding lookahead days we pick the labels generated using the PSY model. We repeat this process with a stride of 3 between the starting lookback dates of previous and next sample. The start (or end) indexes of the bubbles are the index where the bubble started (ended).
We curate zero-shot Reddit data to test our model's ability to generalize across different asset classes and social media platforms. We analyze 12 meme crypto and 17 meme equities (For instance, GameStop, and DOGE) selected based on social media activity over 15 months from  $15^{\mathrm{th}}$  Jan'20 to  $3^{\mathrm{rd}}$  April'21 as shown in Table 8. We mine Reddit posts and comments from top trading subreddits such as r/wallstreetbets using the PushshiftAPI. We scrape daily price data using Yahoo Finance for equities and CoinGecko for cryptos and use the PSY model (Phillips et al., 2015a) to identify bubbles. We follow the same sample generation process that we use for Twitter data. We note that zero-shot data establishes a challenging environment for evaluating CryptoBubbles since it contains varied post lengths and unseen asset classes.
D Data Annotation by Financial Analysts
To further review the ground-truth annotations produced by the PSY model, all annotations were reviewed by five experienced financial analysts achieving a Cohen's  $\kappa$  of 0.93. We recruited these reviewers through an online form, paid them for their work and no ethical considerations were required since we use public financial data. We find that the reviewers agree with the annotations for  $90\%$  of the bubbles. During reviewer disagreement (5% bubbles), we took the majority of all annotators. For  $5\%$  of the bubbles all reviewers agreed that the annotations were incorrect, during which we considered the annotations proposed by the analysts.
Time-series forecasting is a fundamental frontier in deep learning. Hyperbolic learning and RNNs are ubiquitous frameworks that can encode temporal relationships between entities. By advancing existing RNN approaches and providing flexibility for RNNs to capture different intrinsic features from hyperbolic spaces, MBHN can be used to represent a wide range of complex streams of data for various applications such as social influence prediction, healthcare and financial applications. One potential issue of MBHN, like many other RNN based models, is that it provides limited interpretability to its outputs. In the future, we will look into this to enhance the explainability of new time-series forecasting architectures, making such RNN architectures applicable in more critical applications such as medical domains.
<sup>7</sup>https://github.com/pushshift/api
Exchange Coin Tickers
Binance	STX, BNB, REN, NKN, COCOS, HBAR, ORN, ARDR, TRXUP, BAND, WRX, ONE, MTL, EUR, ETHUP, VET, KMD, GTO, LTCUP, XRPUP, FET, SXP, EOSUP, ERD, UMA, PNT, MATIC, ADAUP, BULL, ANKR, RUNE, BZRX, LTO, CHR, WIN, MFT, KSM, STRAX, VITE, ENJ, NULS, BNBUP, KEY, STPT, XTZUP, STMX, SAND, BKRW, TROY, ONT, DOTUP, BEL, LINKUP, AION, GXS, BEAR, FLM, BTCUP, UNIUP, CTSI, DOCK, HIVE, DOTDOWN, DUSK, SUSHI, LINKDOWN, NPXS, PERL, XTZDOWN, TRXDOWN, DOT, ETHDOWN, WAN, BNBDOWN, EOSDOWN, XRPDOWN, ADADOWN, FIO, RVN, CHZ, LTCDOWN, WING, UNIDOWN, HOT, BTCDOWN, COTI, BNBBULL, BNBBEAR
Gateio	OCEAN, PI, DDD, FTI, DRGN, XRPBEAR, WNXM, YFV, IHT, SNET, NBS, MAN, GTC, OCN, SKM, REQ, FIL, LBK, CELR, BLZ, SENC, DKA, LIEN, KIN, OAX, JNT, SBTC, BCN, MDA, EOSBEAR, CDT, OPEN, COFI, HNS, RCN, PEARL, NAX, POWR, ETHBEAR, TFUEL, SWAP, SOP, MBL, FUEL, GAS, REM, MXC, CORN, QSP, RVC, DX, XVG, QLC, ZPT, PCX, HSC, CREAM, OM, AVAX, QKC, ALY, YAM, ADEL, AGS, VTHO, ZSC, COMP, BEAM, BCDN, BU, MKR, ELEC, PST, DOS, XRPBULL, BAT, RFR, DREP, GRIN, DPY, DILI, DOGE, BOT, TOMO, PAY, SMT, QASH, XMC, NEO, EOSDAC, SOUL, GNX, GARD, DCR, YAMV2, ZIL, DATA, SALT, KAVA, GSE, MINI, USDG, MIX, XEM, RLC, MDT, TNT, BCX, NBOT, KLAY, YFI, XTZ, JFI, TAI, GOF, CKB, NAS, BNTY, MET, IOTX, EOSBULL, BTMX, GMAT, LRN, SERO, RED, LEND, OIN, RATING, SFG, DBC, SASHIMI KAI, AMPL, RSV, MTA, MTV, KTON, AR, BTO, PHA, ETHBULL
OKEX	ACT, DIA, CAI, CNTM, ANW, ZEN, ABT, WTC, UGC, WBTC, WXT, INT, XUC, TMTG, CMT, DMD, CVP, QUN, GUSD, RNT, CELO, BLOC, XSR, TCT, HPB, YOU, SUN, STORJ, CRO, EM, BTT, XAS, WGRT,chat, DEP, CIC, HC, RIO, APM, KNC, LINK, MOF, OKB, UTK, MITH, ARK, UBTC, FTM, PPT, XPR, BSV, VIB, JST, NDN, BCD, LRC, FSN, DGD, MXT, DGB, CVT, LBA, SWRV, XPO, BHP, AE, TRADE, CVC, YFII, BTG, FRONT, PRA, PLG, SWFTC, ZYRO, OF, TRUE, DNA, HDAO, BTM, ELF, EGT, YEE, AST, DMG, KCASH, ROAD, SNT, ORBS, APIX, ALV, TOPC, BNT, AERGO, RFUEL, QTUM, SRM, DENT, IQ, DHT
Exchange	Coin Tickers
Bitfinex	FUN, DTX, OMG, XAUT, ESS, EGLD, NEC, GOT, ETC, RRB, XMR, EDO, RRT, LYM, CLO, ANT, XRP, ONL, POA, RIF, GNT, SAN, UNI, ETP, SWM, EOS, BTSE, CND, CNN, PNK, UFR, ZEC, AGI, RINGX, AUC, LEO TRX, XRA, WAX, ETH, DTH, BOX
HuobiPro	HPT, NODE, BIX, KAN, IRIS, SOC, LAMB, NANO, LOL, LOOM, ELA, OGO, AKRO, GT, OGN, TT, WICC, GLM, RSR, VSYS, RUFF, VIDY, BHD, NEW, HBC, PVT, MX, MDS, FIRO, THETA, NEXO, MANA, CRV, HT, LET, FOR, ATP, GXC, DTA, STEEM, CTXC, FTT, NEST, ARPA, IOST, HIT, DF, ITC, MCO, ACH, BTS TRB, WAXP, LUNA, RING
Coinbase	CGLD, REP, NMR, XLM, LTC, ZRX, BTC
Binance USA	ATOM, WAVES, HNT, SOL, ALGO, IOTA
Kraken	ICX, ADA, SC, DAI, PAXG, GBP, BAL, DASH, SNX, MLN, LSK, GNO, BCH, AUD
Gemini	AMP, OXT
Table 7: Exchanges with their corresponding crypto tickers.
Equity	Crypto
AMC	BAN
BB	BASED
BBBY	CAT
BLIAQ	DOGE
CCIV	DOGEC
EXPR	HOGE
JAGX	MONA
KOSS	PPBLZ
LGND	SUSHI
NAKD	TACO
NIO	YAM
NOK	GRLC
PLTR	-
SNDL	-
THCB	-
ZOM	-
GME	-
Table 8: Equity and cryptos used for Reddit Data.